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Sample records for prices forecast comparison aeo

  1. Forecasting metal prices: Do forecasters herd?

    DEFF Research Database (Denmark)

    Pierdzioch, C.; Rulke, J. C.; Stadtmann, G.

    2013-01-01

    We analyze more than 20,000 forecasts of nine metal prices at four different forecast horizons. We document that forecasts are heterogeneous and report that anti-herding appears to be a source of this heterogeneity. Forecaster anti-herding reflects strategic interactions among forecasters...

  2. An empirical comparison of alternative schemes for combining electricity spot price forecasts

    International Nuclear Information System (INIS)

    Nowotarski, Jakub; Raviv, Eran; Trück, Stefan; Weron, Rafał

    2014-01-01

    In this comprehensive empirical study we critically evaluate the use of forecast averaging in the context of electricity prices. We apply seven averaging and one selection scheme and perform a backtesting analysis on day-ahead electricity prices in three major European and US markets. Our findings support the additional benefit of combining forecasts of individual methods for deriving more accurate predictions, however, the performance is not uniform across the considered markets and periods. In particular, equally weighted pooling of forecasts emerges as a simple, yet powerful technique compared with other schemes that rely on estimated combination weights, but only when there is no individual predictor that consistently outperforms its competitors. Constrained least squares regression (CLS) offers a balance between robustness against such well performing individual methods and relatively accurate forecasts, on average better than those of the individual predictors. Finally, some popular forecast averaging schemes – like ordinary least squares regression (OLS) and Bayesian Model Averaging (BMA) – turn out to be unsuitable for predicting day-ahead electricity prices. - Highlights: • So far the most extensive study on combining forecasts for electricity spot prices • 12 stochastic models, 8 forecast combination schemes and 3 markets considered • Our findings support the additional benefit of combining forecasts for deriving more accurate predictions • Methods that allow for unconstrained weights, such as OLS averaging, should be avoided • We recommend a backtesting exercise to identify the preferred forecast averaging method for the data at hand

  3. Uranium price forecasting methods

    International Nuclear Information System (INIS)

    Fuller, D.M.

    1994-01-01

    This article reviews a number of forecasting methods that have been applied to uranium prices and compares their relative strengths and weaknesses. The methods reviewed are: (1) judgemental methods, (2) technical analysis, (3) time-series methods, (4) fundamental analysis, and (5) econometric methods. Historically, none of these methods has performed very well, but a well-thought-out model is still useful as a basis from which to adjust to new circumstances and try again

  4. Forecasting spot electricity prices : Deep learning approaches and empirical comparison of traditional algorithms

    NARCIS (Netherlands)

    Lago Garcia, J.; De Ridder, Fjo; De Schutter, B.H.K.

    2018-01-01

    In this paper, a novel modeling framework for forecasting electricity prices is proposed. While many predictive models have been already proposed to perform this task, the area of deep learning algorithms remains yet unexplored. To fill this scientific gap, we propose four different deep learning

  5. Forecasting regional house price inflation: a comparison between dynamic factor models and vector autoregressive models

    CSIR Research Space (South Africa)

    Das, Sonali

    2010-01-01

    Full Text Available and Gertler, 1995), but also because changes in house prices tend to have important wealth effects on consumption (International Monetary Fund, 2000) and investment (Topel and Rosen, 1988), the importance of forecasting house price infl ation is vital, since... sections, respectively, lay out the DFM and outline the basics of the VAR, the Minnesota-type BVARs, and the SBVARs based on the fi rst-order spatial contiguity (FOSC) and the random walk averaging (RWA) priors developed by LeSage and Pan (1995) and Le...

  6. A Comparison of forecasting Volatility startegies into ARCH Class throughPricing

    OpenAIRE

    Marzia Freo

    2003-01-01

    Daily data on the German market index return are used to consider multiple issues in a forecasting comparison of ARCH-type specifications. first, attention is paid to the impact of different sample sizez, different horizons and fitting of historical versus implied data. Secondly, the issue of volatility transmission is addressed by modelling French and Germany market indexes into simultaneous conditionally heteroskedasticity framework. Errors obtained by updating the Black and Scholes formula...

  7. House Price Forecasts, Forecaster Herding, and the Recent Crisis

    Directory of Open Access Journals (Sweden)

    Christian Pierdzioch

    2012-11-01

    Full Text Available We used the Wall Street Journal survey data for the period 2006–2012 to analyze whether forecasts of house prices and housing starts provide evidence of (anti-herding of forecasters. Forecasts are consistent with herding (anti-herding of forecasters if forecasts are biased towards (away from the consensus forecast. We found that anti-herding is prevalent among forecasters of house prices. We also report that, following the recent crisis, the prevalence of forecaster anti-herding seems to have changed over time.

  8. House Price Forecasts, Forecaster Herding, and the Recent Crisis

    DEFF Research Database (Denmark)

    Stadtmann, Georg; Pierdzioch; Ruelke

    2013-01-01

    We used the Wall Street Journal survey data for the period 2006–2012 to analyze whether forecasts of house prices and housing starts provide evidence of (anti-)herding of forecasters. Forecasts are consistent with herding (anti-herding) of forecasters if forecasts are biased towards (away from) t......) the consensus forecast. We found that anti-herding is prevalent among forecasters of house prices. We also report that, following the recent crisis, the prevalence of forecaster anti-herding seems to have changed over time....

  9. Canadian natural gas price forecast

    International Nuclear Information System (INIS)

    Jones, D.

    1998-01-01

    The basic factors that influenced NYMEX gas prices during the winter of 1997/1998 - warm temperatures, low fuel prices, new production in the Gulf of Mexico, and the fact that forecasters had predicted a mild spring due to El Nino - were reviewed. However, it was noted that for the last 18 months the basic factors had less of an impact on market direction because of an increase in Fund and technical trader participation. Overall, gas prices were strong through most of the year. For the winter of 1998-1999 the prediction was that NYMEX gas prices will remain below $2.00 through to the end of October 1998 because of high U.S. storage levels and moderate temperatures. NYMEX gas prices are expected to peak in January 1999 at $3.25. AECO natural gas prices were predicted to decrease in the short term because of increasing levels of Canadian storage, and because of delays in Northern Border pipeline expansions. It was also predicted that AECO prices will peak in January 1999 and will remain relatively strong through the summer of 1999. tabs., figs

  10. Robust forecast comparison

    OpenAIRE

    Jin, Sainan; Corradi, Valentina; Swanson, Norman

    2015-01-01

    Forecast accuracy is typically measured in terms of a given loss function. However, as a consequence of the use of misspecified models in multiple model comparisons, relative forecast rankings are loss function dependent. This paper addresses this issue by using a novel criterion for forecast evaluation which is based on the entire distribution of forecast errors. We introduce the concepts of general-loss (GL) forecast superiority and convex-loss (CL) forecast superiority, and we establish a ...

  11. Forecasting prices and price volatility in the Nordic electricity market

    International Nuclear Information System (INIS)

    2001-01-01

    We develop a stochastic model for long term price forecasting in a competitive electricity market environment. It is demonstrated both theoretically and through model simulations that non-stochastic models may give biased forecasts both with respect to price level and volatility. In the paper, the model concept is applied on the restructured Nordic electricity market. It is specially in peak load hours that a stochastic model formulation provides significantly different results than an expected value model. (author)

  12. Boosting Learning Algorithm for Stock Price Forecasting

    Science.gov (United States)

    Wang, Chengzhang; Bai, Xiaoming

    2018-03-01

    To tackle complexity and uncertainty of stock market behavior, more studies have introduced machine learning algorithms to forecast stock price. ANN (artificial neural network) is one of the most successful and promising applications. We propose a boosting-ANN model in this paper to predict the stock close price. On the basis of boosting theory, multiple weak predicting machines, i.e. ANNs, are assembled to build a stronger predictor, i.e. boosting-ANN model. New error criteria of the weak studying machine and rules of weights updating are adopted in this study. We select technical factors from financial markets as forecasting input variables. Final results demonstrate the boosting-ANN model works better than other ones for stock price forecasting.

  13. Forecasting Long-Run Electricity Prices

    International Nuclear Information System (INIS)

    Hamm, Gregory; Borison, Adam

    2006-01-01

    Estimation of long-run electricity prices is extremely important but it is also very difficult because of the many uncertainties that will determine future prices, and because of the lack of sufficient historical and forwards data. The difficulty is compounded when forecasters ignore part of the available information or unnecessarily limit their thinking about the future. The authors present a practical approach that addresses these problems. (author)

  14. Housing price forecastability: A factor analysis

    DEFF Research Database (Denmark)

    Møller, Stig Vinther; Bork, Lasse

    2017-01-01

    We examine U.S. housing price forecastability using principal component analysis (PCA), partial least squares (PLS), and sparse PLS (SPLS). We incorporate information from a large panel of 128 economic time series and show that macroeconomic fundamentals have strong predictive power for future...... movements in housing prices. We find that (S)PLS models systematically dominate PCA models. (S)PLS models also generate significant out-of-sample predictive power over and above the predictive power contained by the price-rent ratio, autoregressive benchmarks, and regression models based on small datasets....

  15. Forecasting of electricity prices with neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Gareta, Raquel [Centro de Investigacion de Recursos y Consumos Energeticos (CIRCE), Universidad de Zaragoza, Centro Politecnico Superior, Maria de Luna, 3, 50018 Zaragoza (Spain); Romeo, Luis M. [Centro de Investigacion de Recursos y Consumos Energeticos (CIRCE), Universidad de Zaragoza, Centro Politecnico Superior, Maria de Luna, 3, 50018 Zaragoza (Spain)]. E-mail: luismi@unizar.es; Gil, Antonia [Centro de Investigacion de Recursos y Consumos Energeticos (CIRCE), Universidad de Zaragoza, Centro Politecnico Superior, Maria de Luna, 3, 50018 Zaragoza (Spain)

    2006-08-15

    During recent years, the electricity energy market deregulation has led to a new free competition situation in Europe and other countries worldwide. Generators, distributors and qualified clients have some uncertainties about the future evolution of electricity markets. In consequence, feasibility studies of new generation plants, design of new systems and energy management optimization are frequently postponed. The ability of forecasting energy prices, for instance the electricity prices, would be highly appreciated in order to improve the profitability of utility investments. The development of new simulation techniques, such as Artificial Intelligence (AI), has provided a good tool to forecast time series. In this paper, it is demonstrated that the Neural Network (NN) approach can be used to forecast short term hourly electricity pool prices (for the next day and two or three days after). The NN architecture and design for prices forecasting are described in this paper. The results are tested with extensive data sets, and good agreement is found between actual data and NN results. This methodology could help to improve power plant generation capacity management and, certainly, more profitable operation in daily energy pools.

  16. Forecasting of electricity prices with neural networks

    International Nuclear Information System (INIS)

    Gareta, Raquel; Romeo, Luis M.; Gil, Antonia

    2006-01-01

    During recent years, the electricity energy market deregulation has led to a new free competition situation in Europe and other countries worldwide. Generators, distributors and qualified clients have some uncertainties about the future evolution of electricity markets. In consequence, feasibility studies of new generation plants, design of new systems and energy management optimization are frequently postponed. The ability of forecasting energy prices, for instance the electricity prices, would be highly appreciated in order to improve the profitability of utility investments. The development of new simulation techniques, such as Artificial Intelligence (AI), has provided a good tool to forecast time series. In this paper, it is demonstrated that the Neural Network (NN) approach can be used to forecast short term hourly electricity pool prices (for the next day and two or three days after). The NN architecture and design for prices forecasting are described in this paper. The results are tested with extensive data sets, and good agreement is found between actual data and NN results. This methodology could help to improve power plant generation capacity management and, certainly, more profitable operation in daily energy pools

  17. Crop Insurance Inaccurate FCIC Price Forecasts Increase Program Costs

    National Research Council Canada - National Science Library

    1991-01-01

    ...) how FCIC can improve its forecast accuracy. We found that FCIC's corn, wheat, and soybeans price forecasts exhibit large bias errors that exceed those of other available alternative forecasts and that FCIC would have spent...

  18. Electricity price forecasting in deregulated markets: A review and evaluation

    Energy Technology Data Exchange (ETDEWEB)

    Aggarwal, Sanjeev Kumar; Saini, Lalit Mohan; Kumar, Ashwani [Department of Electrical Engineering, National Institute of Technology, Kurukshetra, Haryana (India)

    2009-01-15

    The main methodologies used in electricity price forecasting have been reviewed in this paper. The following price-forecasting techniques have been covered: (i) stochastic time series, (ii) causal models, and (iii) artificial intelligence based models. The quantitative analysis of the work done by various authors has been presented based on (a) time horizon for prediction, (b) input variables, (c) output variables, (d) results, (e) data points used for analysis, (f) preprocessing technique employed, and (g) architecture of the model. The results have been presented in the form of tables for ease of comparison. Classification of various price-influencing factors used by different researchers has been done and put for reference. Application of various models as applied to different electricity markets is also presented for consideration. (author)

  19. Electricity price forecasting in deregulated markets: A review and evaluation

    International Nuclear Information System (INIS)

    Aggarwal, Sanjeev Kumar; Saini, Lalit Mohan; Kumar, Ashwani

    2009-01-01

    The main methodologies used in electricity price forecasting have been reviewed in this paper. The following price-forecasting techniques have been covered: (i) stochastic time series, (ii) causal models, and (iii) artificial intelligence based models. The quantitative analysis of the work done by various authors has been presented based on (a) time horizon for prediction, (b) input variables, (c) output variables, (d) results, (e) data points used for analysis, (f) preprocessing technique employed, and (g) architecture of the model. The results have been presented in the form of tables for ease of comparison. Classification of various price-influencing factors used by different researchers has been done and put for reference. Application of various models as applied to different electricity markets is also presented for consideration. (author)

  20. Electricity price forecasting through transfer function models

    International Nuclear Information System (INIS)

    Nogales, F.J.; Conejo, A.J.

    2006-01-01

    Forecasting electricity prices in present day competitive electricity markets is a must for both producers and consumers because both need price estimates to develop their respective market bidding strategies. This paper proposes a transfer function model to predict electricity prices based on both past electricity prices and demands, and discuss the rationale to build it. The importance of electricity demand information is assessed. Appropriate metrics to appraise prediction quality are identified and used. Realistic and extensive simulations based on data from the PJM Interconnection for year 2003 are conducted. The proposed model is compared with naive and other techniques. Journal of the Operational Research Society (2006) 57, 350-356.doi:10.1057/palgrave.jors.2601995; published online 18 May 2005. (author)

  1. A Statistical Approach for Interval Forecasting of the Electricity Price

    DEFF Research Database (Denmark)

    Zhao, Jun Hua; Dong, Zhao Yang; Xu, Zhao

    2008-01-01

    the prediction interval is essential for estimating the uncertainty involved in the price and thus is highly useful for making generation bidding strategies and investment decisions. In this paper, a novel data mining-based approach is proposed to achieve two major objectives: 1) to accurately forecast the value......Electricity price forecasting is a difficult yet essential task for market participants in a deregulated electricity market. Rather than forecasting the value, market participants are sometimes more interested in forecasting the prediction interval of the electricity price. Forecasting...... of the electricity price series, which is widely accepted as a nonlinear time series; 2) to accurately estimate the prediction interval of the electricity price series. In the proposed approach, support vector machine (SVM) is employed to forecast the value of the price. To forecast the prediction interval, we...

  2. Average regional end-use energy price projections to the year 2030

    International Nuclear Information System (INIS)

    1991-01-01

    The energy prices shown in this report cover the period from 1991 through 2030. These prices reflect sector/fuel price projections from the Annual Energy Outlook 1991 (AEO) base case, developed using the Energy Information Administration's (EIA) Intermediate Future Forecasting System (IFFS) forecasting model. Projections through 2010 are AEO base case forecasts. Projections for the period from 2011 through 2030 were developed separately from the AEO for this report, and the basis for these projections is described in Chapter 3. Projections in this report include average energy prices for each of four Census Regions for the residential, commercial, industrial, and transportation end-use sectors. Energy sources include electricity, distillate fuel oil, liquefied petroleum gas, motor gasoline, residual fuel oil, natural gas, and steam coal. (VC)

  3. The Combination Forecasting of Electricity Price Based on Price Spikes Processing: A Case Study in South Australia

    Directory of Open Access Journals (Sweden)

    Jianzhou Wang

    2014-01-01

    Full Text Available Electricity price forecasting holds very important position in the electricity market. Inaccurate price forecasting may cause energy waste and management chaos in the electricity market. However, electricity price forecasting has always been regarded as one of the largest challenges in the electricity market because it shows high volatility, which makes electricity price forecasting difficult. This paper proposes the use of artificial intelligence optimization combination forecasting models based on preprocessing data, called “chaos particles optimization (CPSO weight-determined combination models.” These models allow for the weight of the combined model to take values of [-1,1]. In the proposed models, the density-based spatial clustering of applications with noise (DBSCAN algorithm is used to identify outliers, and the outliers are replaced by a new data-produced linear interpolation function. The proposed CPSO weight-determined combination models are then used to forecast the projected future electricity price. In this case study, the electricity price data of South Australia are simulated. The results indicate that, while the weight of the combined model takes values of [-1,1], the proposed combination model can always provide adaptive, reliable, and comparatively accurate forecast results in comparison to traditional combination models.

  4. Application of Markov Model in Crude Oil Price Forecasting

    Directory of Open Access Journals (Sweden)

    Nuhu Isah

    2017-08-01

    Full Text Available Crude oil is an important energy commodity to mankind. Several causes have made crude oil prices to be volatile. The fluctuation of crude oil prices has affected many related sectors and stock market indices. Hence, forecasting the crude oil prices is essential to avoid the future prices of the non-renewable natural resources to rise. In this study, daily crude oil prices data was obtained from WTI dated 2 January to 29 May 2015. We used Markov Model (MM approach in forecasting the crude oil prices. In this study, the analyses were done using EViews and Maple software where the potential of this software in forecasting daily crude oil prices time series data was explored. Based on the study, we concluded that MM model is able to produce accurate forecast based on a description of history patterns in crude oil prices.

  5. A method for short term electricity spot price forecasting

    International Nuclear Information System (INIS)

    Koreneff, G.; Seppaelae, A.; Lehtonen, M.; Kekkonen, V.; Laitinen, E.; Haekli, J.; Antila, E.

    1998-01-01

    In Finland, the electricity market was de-regulated in November 1995. For the electricity purchase of power companies this has caused big changes, since the old tariff based contracts of bulk power supply have been replaced by negotiated bilateral short term contracts and by power purchase from the spot market. In the spot market, in turn, there are at the present two strong actors: The electricity exchange of Finland and the Nordic power pool which is run by the Swedish and Norwegian companies. Today, the power companies in Finland have short term trade with both of the electricity exchanges. The aim of this chapter is to present methods for spot price forecasting in the electricity exchange. The main focus is given to the Finnish circumstances. In the beginning of the presentation, the practices of the electricity exchange of Finland are described, and a brief presentation is given on the different contracts, or electricity products, available in the spot market. For comparison, the practices of the Nordic electricity exchange are also outlined. A time series technique for spot price forecasting is presented. The structure of the model is presented, and its validity is tested using real case data obtained from the Finnish power market. The spot price forecasting model is a part of a computer system for distribution energy management (DEM) in a de-regulated power market

  6. A method for short term electricity spot price forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Koreneff, G; Seppaelae, A; Lehtonen, M; Kekkonen, V [VTT Energy, Espoo (Finland); Laitinen, E; Haekli, J [Vaasa Univ. (Finland); Antila, E [ABB Transmit Oy (Finland)

    1998-08-01

    In Finland, the electricity market was de-regulated in November 1995. For the electricity purchase of power companies this has caused big changes, since the old tariff based contracts of bulk power supply have been replaced by negotiated bilateral short term contracts and by power purchase from the spot market. In the spot market, in turn, there are at the present two strong actors: The electricity exchange of Finland and the Nordic power pool which is run by the Swedish and Norwegian companies. Today, the power companies in Finland have short term trade with both of the electricity exchanges. The aim of this chapter is to present methods for spot price forecasting in the electricity exchange. The main focus is given to the Finnish circumstances. In the beginning of the presentation, the practices of the electricity exchange of Finland are described, and a brief presentation is given on the different contracts, or electricity products, available in the spot market. For comparison, the practices of the Nordic electricity exchange are also outlined. A time series technique for spot price forecasting is presented. The structure of the model is presented, and its validity is tested using real case data obtained from the Finnish power market. The spot price forecasting model is a part of a computer system for distribution energy management (DEM) in a de-regulated power market

  7. Evaluating information in multiple horizon forecasts. The DOE's energy price forecasts

    International Nuclear Information System (INIS)

    Sanders, Dwight R.; Manfredo, Mark R.; Boris, Keith

    2009-01-01

    The United States Department of Energy's (DOE) quarterly price forecasts for energy commodities are examined to determine the incremental information provided at the one-through four-quarter forecast horizons. A direct test for determining information content at alternative forecast horizons, developed by Vuchelen and Gutierrez [Vuchelen, J. and Gutierrez, M.-I. 'A Direct Test of the Information Content of the OECD Growth Forecasts.' International Journal of Forecasting. 21(2005):103-117.], is used. The results suggest that the DOE's price forecasts for crude oil, gasoline, and diesel fuel do indeed provide incremental information out to three-quarters ahead, while natural gas and electricity forecasts are informative out to the four-quarter horizon. In contrast, the DOE's coal price forecasts at two-, three-, and four-quarters ahead provide no incremental information beyond that provided for the one-quarter horizon. Recommendations of how to use these results for making forecast adjustments is also provided. (author)

  8. Deriving inflation forecasts from government bond prices

    Directory of Open Access Journals (Sweden)

    Kožul Nataša

    2014-01-01

    Full Text Available In financial research and practice, it is widely accepted that nominal interest rates derived from the prices of various financial products of different maturities comprise of corresponding real interest rates and inflation. While extensive research has been conducted on the relationship between these three variables, estimation of their levels is still largely based on the industry surveys and market data. As this information only indicates the current expectations of interest rate and inflation movements over time, a number of caveats should be noted when interpreting such measures. In the US and the UK, where the government bond markets are the largest and most active, a comparative analysis between conventional government bonds and those whose yield is linked to inflation provides a measure of inflation expectations. However, as such analyses implicitly assume that investment in government bonds is virtually risk free, it is questionable whether the derived estimates are of any value in current economic conditions. Moreover, this approach cannot be generalized to other countries, where number of traded products from which any relationship between interest rates and inflation can be determined is limited and different economic conditions prevail. Thus, this paper aims to present an overview of the methodologies used to forecast inflation rates from government bond prices, drawing attention to the key assumptions and limitations of these approaches. The goal is to ascertain their accuracy, and thus their value in determining the real yields of various interest rate-linked products.

  9. Electricity Price Forecasting Based on AOSVR and Outlier Detection

    Institute of Scientific and Technical Information of China (English)

    Zhou Dianmin; Gao Lin; Gao Feng

    2005-01-01

    Electricity price is of the first consideration for all the participants in electric power market and its characteristics are related to both market mechanism and variation in the behaviors of market participants. It is necessary to build a real-time price forecasting model with adaptive capability; and because there are outliers in the price data, they should be detected and filtrated in training the forecasting model by regression method. In view of these points, this paper presents an electricity price forecasting method based on accurate on-line support vector regression (AOSVR) and outlier detection. Numerical testing results show that the method is effective in forecasting the electricity prices in electric power market.

  10. Effect of the accuracy of price forecasting on profit in a Price Based Unit Commitment

    International Nuclear Information System (INIS)

    Delarue, Erik; Van Den Bosch, Pieterjan; D'haeseleer, William

    2010-01-01

    This paper discusses and quantifies the so-called loss of profit (i.e., the sub-optimality of profit) that can be expected in a Price Based Unit Commitment (PBUC), when incorrect price forecasts are used. For this purpose, a PBUC model has been developed and utilized, using Mixed Integer Linear Programming (MILP). Simulations are used to determine the relationship between the Mean Absolute Percentage Error (MAPE) of a certain price forecast and the loss of profit, for four different types of power plants. A Combined Cycle (CC) power plant and a pumped storage unit show highest sensitivity to incorrect forecasts. A price forecast with a MAPE of 15%, on average, yields 13.8% and 12.1% profit loss, respectively. A classic thermal power plant (coal fired) and cascade hydro unit are less affected by incorrect forecasts, with only 2.4% and 2.0% profit loss, respectively, at the same price forecast MAPE. This paper further demonstrates that if price forecasts show an average bias (upward or downward), using the MAPE as measure of the price forecast might not be sufficient to quantify profit loss properly. Profit loss in this case has been determined as a function of both shift and MAPE of the price forecast. (author)

  11. Density Forecasts of Crude-Oil Prices Using Option-Implied and ARCH-Type Models

    DEFF Research Database (Denmark)

    Tsiaras, Leonidas; Høg, Esben

      The predictive accuracy of competing crude-oil price forecast densities is investigated for the 1994-2006 period. Moving beyond standard ARCH models that rely exclusively on past returns, we examine the benefits of utilizing the forward-looking information that is embedded in the prices...... as for regions and intervals that are of special interest for the economic agent. We find that non-parametric adjustments of risk-neutral density forecasts perform significantly better than their parametric counterparts. Goodness-of-fit tests and out-of-sample likelihood comparisons favor forecast densities...

  12. Formation of an Integrated Stock Price Forecast Model in Lithuania

    Directory of Open Access Journals (Sweden)

    Audrius Dzikevičius

    2016-12-01

    Full Text Available Technical and fundamental analyses are widely used to forecast stock prices due to lack of knowledge of other modern models and methods such as Residual Income Model, ANN-APGARCH, Support Vector Machine, Probabilistic Neural Network and Genetic Fuzzy Systems. Although stock price forecast models integrating both technical and fundamental analyses are currently used widely, their integration is not justified comprehensively enough. This paper discusses theoretical one-factor and multi-factor stock price forecast models already applied by investors at a global level and determines possibility to create and apply practically a stock price forecast model which integrates fundamental and technical analysis with the reference to the Lithuanian stock market. The research is aimed to determine the relationship between stock prices of the 14 Lithuanian companies listed in the Main List by the Nasdaq OMX Baltic and various fundamental variables. Based on correlation and regression analysis results and application of c-Squared Test, ANOVA method, a general stock price forecast model is generated. This paper discusses practical implications how the developed model can be used to forecast stock prices by individual investors and suggests additional check measures.

  13. Electricity price forecast using Combinatorial Neural Network trained by a new stochastic search method

    International Nuclear Information System (INIS)

    Abedinia, O.; Amjady, N.; Shafie-khah, M.; Catalão, J.P.S.

    2015-01-01

    Highlights: • Presenting a Combinatorial Neural Network. • Suggesting a new stochastic search method. • Adapting the suggested method as a training mechanism. • Proposing a new forecast strategy. • Testing the proposed strategy on real-world electricity markets. - Abstract: Electricity price forecast is key information for successful operation of electricity market participants. However, the time series of electricity price has nonlinear, non-stationary and volatile behaviour and so its forecast method should have high learning capability to extract the complex input/output mapping function of electricity price. In this paper, a Combinatorial Neural Network (CNN) based forecasting engine is proposed to predict the future values of price data. The CNN-based forecasting engine is equipped with a new training mechanism for optimizing the weights of the CNN. This training mechanism is based on an efficient stochastic search method, which is a modified version of chemical reaction optimization algorithm, giving high learning ability to the CNN. The proposed price forecast strategy is tested on the real-world electricity markets of Pennsylvania–New Jersey–Maryland (PJM) and mainland Spain and its obtained results are extensively compared with the results obtained from several other forecast methods. These comparisons illustrate effectiveness of the proposed strategy.

  14. Forecasting Day-Ahead Electricity Prices : Utilizing Hourly Prices

    NARCIS (Netherlands)

    E. Raviv (Eran); K.E. Bouwman (Kees); D.J.C. van Dijk (Dick)

    2013-01-01

    textabstractThe daily average price of electricity represents the price of electricity to be delivered over the full next day and serves as a key reference price in the electricity market. It is an aggregate that equals the average of hourly prices for delivery during each of the 24 individual

  15. Forecasting Day-Ahead Electricity Prices: Utilizing Hourly Prices

    OpenAIRE

    Raviv, Eran; Bouwman, Kees E.; van Dijk, Dick

    2013-01-01

    This discussion paper led to a publication in 'Energy Economics' , 2015, 50, 227-239. The daily average price of electricity represents the price of electricity to be delivered over the full next day and serves as a key reference price in the electricity market. It is an aggregate that equals the average of hourly prices for delivery during each of the 24 individual hours. This paper demonstrates that the disaggregated hourly prices contain useful predictive information for the daily average ...

  16. Design and implementation of ticket price forecasting system

    Science.gov (United States)

    Li, Yuling; Li, Zhichao

    2018-05-01

    With the advent of the aviation travel industry, a large number of data mining technologies have been developed to increase profits for airlines in the past two decades. The implementation of the digital optimization strategy leads to price discrimination, for example, similar seats on the same flight are purchased at different prices, depending on the time of purchase, the supplier, and so on. Price fluctuations make the prediction of ticket prices have application value. In this paper, a combination of ARMA algorithm and random forest algorithm is proposed to predict the price of air ticket. The experimental results show that the model is more reliable by comparing the forecasting results with the actual results of each price model. The model is helpful for passengers to buy tickets and to save money. Based on the proposed model, using Python language and SQL Server database, we design and implement the ticket price forecasting system.

  17. Forecasting the price of gold: An error correction approach

    Directory of Open Access Journals (Sweden)

    Kausik Gangopadhyay

    2016-03-01

    Full Text Available Gold prices in the Indian market may be influenced by a multitude of factors such as the value of gold in investment decisions, as an inflation hedge, and in consumption motives. We develop a model to explain and forecast gold prices in India, using a vector error correction model. We identify investment decision and inflation hedge as prime movers of the data. We also present out-of-sample forecasts of our model and the related properties.

  18. Forecasting electricity market pricing using artificial neural networks

    International Nuclear Information System (INIS)

    Pao, Hsiao-Tien

    2007-01-01

    Electricity price forecasting is extremely important for all market players, in particular for generating companies: in the short term, they must set up bids for the spot market; in the medium term, they have to define contract policies; and in the long term, they must define their expansion plans. For forecasting long-term electricity market pricing, in order to avoid excessive round-off and prediction errors, this paper proposes a new artificial neural network (ANN) with single output node structure by using direct forecasting approach. The potentials of ANNs are investigated by employing a rolling cross validation scheme. Out of sample performance evaluated with three criteria across five forecasting horizons shows that the proposed ANNs are a more robust multi-step ahead forecasting method than autoregressive error models. Moreover, ANN predictions are quite accurate even when the length of the forecast horizon is relatively short or long

  19. Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting

    Directory of Open Access Journals (Sweden)

    Bijay Neupane

    2017-01-01

    Full Text Available Forecasting of electricity prices is important in deregulated electricity markets for all of the stakeholders: energy wholesalers, traders, retailers and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards the aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participates in forecasting 1-h ahead the price for each hour of a day. We propose two different strategies, namely, the Fixed Weight Method (FWM and the Varying Weight Method (VWM, for selecting each hour’s expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features extracted from the past electricity price data, weather data and calendar data. The proposed ensemble model offers better results than the Autoregressive Integrated Moving Average (ARIMA method, the Pattern Sequence-based Forecasting (PSF method and our previous work using Artificial Neural Networks (ANN alone on the datasets for New York, Australian and Spanish electricity markets.

  20. Probabilistic Electricity Price Forecasting Models by Aggregation of Competitive Predictors

    Directory of Open Access Journals (Sweden)

    Claudio Monteiro

    2018-04-01

    Full Text Available This article presents original probabilistic price forecasting meta-models (PPFMCP models, by aggregation of competitive predictors, for day-ahead hourly probabilistic price forecasting. The best twenty predictors of the EEM2016 EPF competition are used to create ensembles of hourly spot price forecasts. For each hour, the parameter values of the probability density function (PDF of a Beta distribution for the output variable (hourly price can be directly obtained from the expected and variance values associated to the ensemble for such hour, using three aggregation strategies of predictor forecasts corresponding to three PPFMCP models. A Reliability Indicator (RI and a Loss function Indicator (LI are also introduced to give a measure of uncertainty of probabilistic price forecasts. The three PPFMCP models were satisfactorily applied to the real-world case study of the Iberian Electricity Market (MIBEL. Results from PPFMCP models showed that PPFMCP model 2, which uses aggregation by weight values according to daily ranks of predictors, was the best probabilistic meta-model from a point of view of mean absolute errors, as well as of RI and LI. PPFMCP model 1, which uses the averaging of predictor forecasts, was the second best meta-model. PPFMCP models allow evaluations of risk decisions based on the price to be made.

  1. Accounting for fuel price risk: Using forward natural gas prices instead of gas price forecasts to compare renewable to natural gas-fired generation

    Energy Technology Data Exchange (ETDEWEB)

    Bolinger, Mark; Wiser, Ryan; Golove, William

    2003-08-13

    Against the backdrop of increasingly volatile natural gas prices, renewable energy resources, which by their nature are immune to natural gas fuel price risk, provide a real economic benefit. Unlike many contracts for natural gas-fired generation, renewable generation is typically sold under fixed-price contracts. Assuming that electricity consumers value long-term price stability, a utility or other retail electricity supplier that is looking to expand its resource portfolio (or a policymaker interested in evaluating different resource options) should therefore compare the cost of fixed-price renewable generation to the hedged or guaranteed cost of new natural gas-fired generation, rather than to projected costs based on uncertain gas price forecasts. To do otherwise would be to compare apples to oranges: by their nature, renewable resources carry no natural gas fuel price risk, and if the market values that attribute, then the most appropriate comparison is to the hedged cost of natural gas-fired generation. Nonetheless, utilities and others often compare the costs of renewable to gas-fired generation using as their fuel price input long-term gas price forecasts that are inherently uncertain, rather than long-term natural gas forward prices that can actually be locked in. This practice raises the critical question of how these two price streams compare. If they are similar, then one might conclude that forecast-based modeling and planning exercises are in fact approximating an apples-to-apples comparison, and no further consideration is necessary. If, however, natural gas forward prices systematically differ from price forecasts, then the use of such forecasts in planning and modeling exercises will yield results that are biased in favor of either renewable (if forwards < forecasts) or natural gas-fired generation (if forwards > forecasts). In this report we compare the cost of hedging natural gas price risk through traditional gas-based hedging instruments (e

  2. Coal Price Forecasting and Structural Analysis in China

    Directory of Open Access Journals (Sweden)

    Xiaopeng Guo

    2016-01-01

    Full Text Available Coal plays an important role in China’s energy structure and its price has been continuously decreasing since the second half of 2012. Constant low price of coal affected the profits of coal enterprises and the coal use of its downstream firms; the precision of coal price provides a reference for these enterprises making their management strategy. Based on the historical data of coal price and related factors such as port stocks, sales volume, futures prices, Producer Price Index (PPI, and crude oil price rate from November 2013 to June 2016, this study aims to forecast coal price using vector autoregression (VAR model and portray the dynamic correlations between coal price and variables by the impulse response function and variance decomposition. Comparing predicted and actual values, the root mean square error (RMSE was small which indicated good precision of this model. Thus this short period prediction can help these enterprises make the right business decisions.

  3. Forecasting short-term power prices in the Ontario Electricity Market (OEM) with a fuzzy logic based inference system

    International Nuclear Information System (INIS)

    Arciniegas, Alvaro I.; Arciniegas Rueda, Ismael E.

    2008-01-01

    The Ontario Electricity Market (OEM), which opened in May 2002, is relatively new and is still under change. In addition, the bidding strategies of the participants are such that the relationships between price and fundamentals are non-linear and dynamic. The lack of market maturity and high complexity hinders the use of traditional statistical methodologies (e.g., regression analysis) for price forecasting. Therefore, a flexible model is needed to achieve good forecasting in OEM. This paper uses a Takagi-Sugeno-Kang (TSK) fuzzy inference system in forecasting the one-day-ahead real-time peak price of the OEM. The forecasting results of TSK are compared with those obtained by traditional statistical and neural network based forecasting. The comparison suggests that TSK has considerable value in forecasting one-day-ahead peak price in OEM. (author)

  4. A hybrid approach for probabilistic forecasting of electricity price

    DEFF Research Database (Denmark)

    Wan, Can; Xu, Zhao; Wang, Yelei

    2014-01-01

    to the nonstationarities involved in market clearing prices (MCPs), it is rather difficult to accurately predict MCPs in advance. The challenge is getting intensified as more and more renewable energy and other new technologies emerged in smart grids. Therefore transformation from traditional point forecasts...... electricity price forecasting is proposed in this paper. The effectiveness of the proposed hybrid method has been validated through comprehensive tests using real price data from Australian electricity market.......The electricity market plays a key role in realizing the economic prophecy of smart grids. Accurate and reliable electricity market price forecasting is essential to facilitate various decision making activities of market participants in the future smart grid environment. However, due...

  5. Price Forecasting of Electricity Markets in the Presence of a High Penetration of Wind Power Generators

    OpenAIRE

    Saber Talari; Miadreza Shafie-khah; Gerardo J. Osório; Fei Wang; Alireza Heidari; João P. S. Catalão

    2017-01-01

    Price forecasting plays a vital role in the day-ahead markets. Once sellers and buyers access an accurate price forecasting, managing the economic risk can be conducted appropriately through offering or bidding suitable prices. In networks with high wind power penetration, the electricity price is influenced by wind energy; therefore, price forecasting can be more complicated. This paper proposes a novel hybrid approach for price forecasting of day-ahead markets, with high penetration of wind...

  6. Short-term electricity price forecast based on the improved hybrid model

    International Nuclear Information System (INIS)

    Dong Yao; Wang Jianzhou; Jiang He; Wu Jie

    2011-01-01

    Highlights: → The proposed models can detach high volatility and daily seasonality of electricity price. → The improved hybrid forecast models can make full use of the advantages of individual models. → The proposed models create commendable improvements that are relatively satisfactorily for current research. → The proposed models do not require making complicated decisions about the explicit form. - Abstract: Half-hourly electricity price in power system are volatile, electricity price forecast is significant information which can help market managers and participants involved in electricity market to prepare their corresponding bidding strategies to maximize their benefits and utilities. However, the fluctuation of electricity price depends on the common effect of many factors and there is a very complicated random in its evolution process. Therefore, it is difficult to forecast half-hourly prices with traditional only one model for different behaviors of half-hourly prices. This paper proposes the improved forecasting model that detaches high volatility and daily seasonality for electricity price of New South Wales in Australia based on Empirical Mode Decomposition, Seasonal Adjustment and Autoregressive Integrated Moving Average. The prediction errors are analyzed and compared with the ones obtained from the traditional Seasonal Autoregressive Integrated Moving Average model. The comparisons demonstrate that the proposed model can improve the prediction accuracy noticeably.

  7. Short-term electricity price forecast based on the improved hybrid model

    Energy Technology Data Exchange (ETDEWEB)

    Dong Yao, E-mail: dongyao20051987@yahoo.cn [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China); Wang Jianzhou, E-mail: wjz@lzu.edu.cn [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China); Jiang He; Wu Jie [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China)

    2011-08-15

    Highlights: {yields} The proposed models can detach high volatility and daily seasonality of electricity price. {yields} The improved hybrid forecast models can make full use of the advantages of individual models. {yields} The proposed models create commendable improvements that are relatively satisfactorily for current research. {yields} The proposed models do not require making complicated decisions about the explicit form. - Abstract: Half-hourly electricity price in power system are volatile, electricity price forecast is significant information which can help market managers and participants involved in electricity market to prepare their corresponding bidding strategies to maximize their benefits and utilities. However, the fluctuation of electricity price depends on the common effect of many factors and there is a very complicated random in its evolution process. Therefore, it is difficult to forecast half-hourly prices with traditional only one model for different behaviors of half-hourly prices. This paper proposes the improved forecasting model that detaches high volatility and daily seasonality for electricity price of New South Wales in Australia based on Empirical Mode Decomposition, Seasonal Adjustment and Autoregressive Integrated Moving Average. The prediction errors are analyzed and compared with the ones obtained from the traditional Seasonal Autoregressive Integrated Moving Average model. The comparisons demonstrate that the proposed model can improve the prediction accuracy noticeably.

  8. Improving the Forecasting Accuracy of Crude Oil Prices

    Directory of Open Access Journals (Sweden)

    Xuluo Yin

    2018-02-01

    Full Text Available Currently, oil is the key element of energy sustainability, and its prices and economy have a strong mutual influence. Modeling a good method to accurately predict oil prices over long future horizons is challenging and of great interest to investors and policymakers. This paper forecasts oil prices using many predictor variables with a new time-varying weight combination approach. In doing so, we first use five single-variable time-varying parameter models to predict crude oil prices separately. Second, every special model is assigned a time-varying weight by the new combination approach. Finally, the forecasting results of oil prices are calculated. The results show that the paper’s method is robust and performs well compared to random walk.

  9. Structural change and forecasting long-run energy prices

    International Nuclear Information System (INIS)

    Bernard, J.T.; Khalaf, L.

    2004-01-01

    Fluctuating energy prices have a significant impact on the economies of industrialized nations. A recent study has shown a strong non-linear relationship between changes in oil prices and growth in gross domestic product (GDP). In order to forecast the behaviour of energy prices, a complete model must take into account domestic and international supply and demand conditions, market regulations, technological advances and geopolitics. In 1999, Pindyck suggested that for long-term forecasting, a simple model should be adopted where prices grow in real terms and at a fixed rate. This paper tests the statistical significance of Pindyck's suggested class of econometric equations that model the behaviour of long-run real energy prices. The models assume mean-reverting prices with continuous and random changes in their level and trend. They are estimated using Kalman filtering. The authors used simulation-based procedures to address the issue of non-standard test statistics and nuisance parameters. Results were reported for a standard Monte Carlo test and a maximized Monte Carlo test. Results shown statistically significant instabilities for coal and natural gas prices, but not for crude oil prices. Various models were differentiated using out-of-sample forecasting exercises. 25 refs., 3 tabs

  10. Day ahead price forecasting of electricity markets by a mixed data model and hybrid forecast method

    International Nuclear Information System (INIS)

    Amjady, Nima; Keynia, Farshid

    2008-01-01

    In a competitive electricity market, forecast of energy prices is a key information for the market participants. However, price signal usually has a complex behavior due to its nonlinearity, nonstationarity, and time variancy. In spite of all performed researches on this area in the recent years, there is still an essential need for more accurate and robust price forecast methods. In this paper, a combination of wavelet transform (WT) and a hybrid forecast method is proposed for this purpose. The hybrid method is composed of cascaded forecasters where each forecaster consists of a neural network (NN) and an evolutionary algorithms (EA). Both time domain and wavelet domain features are considered in a mixed data model for price forecast, in which the candidate input variables are refined by a feature selection technique. The adjustable parameters of the whole method are fine-tuned by a cross-validation technique. The proposed method is examined on PJM electricity market and compared with some of the most recent price forecast methods. (author)

  11. Energy price forecast by market analysis

    International Nuclear Information System (INIS)

    Jongepier, A.G.

    2000-01-01

    A power trader benefits from accurate price predictions. Based on market analyses, KEMA Connect has developed - in cooperation with Essent Energy Trading - a market model, enhancing the insight into market operation and one's own actions and thus resulting in accurate price predictions

  12. Modelling and Forecasting Stock Price Movements with Serially Dependent Determinants

    Directory of Open Access Journals (Sweden)

    Rasika Yatigammana

    2018-05-01

    Full Text Available The direction of price movements are analysed under an ordered probit framework, recognising the importance of accounting for discreteness in price changes. By extending the work of Hausman et al. (1972 and Yang and Parwada (2012,This paper focuses on improving the forecast performance of the model while infusing a more practical perspective by enhancing flexibility. This is achieved by extending the existing framework to generate short term multi period ahead forecasts for better decision making, whilst considering the serial dependence structure. This approach enhances the flexibility and adaptability of the model to future price changes, particularly targeting risk minimisation. Empirical evidence is provided, based on seven stocks listed on the Australian Securities Exchange (ASX. The prediction success varies between 78 and 91 per cent for in-sample and out-of-sample forecasts for both the short term and long term.

  13. Forecasting loads and prices in competitive power markets

    International Nuclear Information System (INIS)

    Bunn, D.W.

    2000-01-01

    This paper provides a review of some of the main methodological issues and techniques which have become innovative in addressing the problem of forecasting daily loads and prices in the new competitive power markets. Particular emphasis is placed upon computationally intensive methods, including variable segmentation, multiple modeling, combinations, and neural networks for forecasting the demand side, and strategic simulation using artificial agents for the supply side

  14. New evidence of anti-herding of oil-price forecasters

    International Nuclear Information System (INIS)

    Pierdzioch, Christian; Ruelke, Jan Christoph; Stadtmann, Georg

    2010-01-01

    We used the oil-price forecasts of the Survey of Professional Forecasters published by the European Central Bank to analyze whether oil-price forecasters herd or anti-herd. Oil-price forecasts are consistent with herding (anti-herding) of forecasters if forecasts are biased towards (away from) the consensus forecast. Based on a new empirical test developed by Bernhardt et al. (J. Financ. Econ. 80: 657-675, 2006), we found strong evidence of anti-herding among oil-price forecasters. (author)

  15. Forecasting Natural Rubber Price In Malaysia Using Arima

    Science.gov (United States)

    Zahari, Fatin Z.; Khalid, Kamil; Roslan, Rozaini; Sufahani, Suliadi; Mohamad, Mahathir; Saifullah Rusiman, Mohd; Ali, Maselan

    2018-04-01

    This paper contains introduction, materials and methods, results and discussions, conclusions and references. Based on the title mentioned, high volatility of the price of natural rubber nowadays will give the significant risk to the producers, traders, consumers, and others parties involved in the production of natural rubber. To help them in making decisions, forecasting is needed to predict the price of natural rubber. The main objective of the research is to forecast the upcoming price of natural rubber by using the reliable statistical method. The data are gathered from Malaysia Rubber Board which the data are from January 2000 until December 2015. In this research, average monthly price of Standard Malaysia Rubber 20 (SMR20) will be forecast by using Box-Jenkins approach. Time series plot is used to determine the pattern of the data. The data have trend pattern which indicates the data is non-stationary data and the data need to be transformed. By using the Box-Jenkins method, the best fit model for the time series data is ARIMA (1, 1, 0) which this model satisfy all the criteria needed. Hence, ARIMA (1, 1, 0) is the best fitted model and the model will be used to forecast the average monthly price of Standard Malaysia Rubber 20 (SMR20) for twelve months ahead.

  16. Electricity price forecasting using Enhanced Probability Neural Network

    International Nuclear Information System (INIS)

    Lin, Whei-Min; Gow, Hong-Jey; Tsai, Ming-Tang

    2010-01-01

    This paper proposes a price forecasting system for electric market participants to reduce the risk of price volatility. Combining the Probability Neural Network (PNN) and Orthogonal Experimental Design (OED), an Enhanced Probability Neural Network (EPNN) is proposed in the solving process. In this paper, the Locational Marginal Price (LMP), system load and temperature of PJM system were collected and the data clusters were embedded in the Excel Database according to the year, season, workday, and weekend. With the OED to smooth parameters in the EPNN, the forecasting error can be improved during the training process to promote the accuracy and reliability where even the ''spikes'' can be tracked closely. Simulation results show the effectiveness of the proposed EPNN to provide quality information in a price volatile environment. (author)

  17. Forecasting oil price trends using wavelets and hidden Markov models

    International Nuclear Information System (INIS)

    Souza e Silva, Edmundo G. de; Souza e Silva, Edmundo A. de; Legey, Luiz F.L.

    2010-01-01

    The crude oil price is influenced by a great number of factors, most of which interact in very complex ways. For this reason, forecasting it through a fundamentalist approach is a difficult task. An alternative is to use time series methodologies, with which the price's past behavior is conveniently analyzed, and used to predict future movements. In this paper, we investigate the usefulness of a nonlinear time series model, known as hidden Markov model (HMM), to predict future crude oil price movements. Using an HMM, we develop a forecasting methodology that consists of, basically, three steps. First, we employ wavelet analysis to remove high frequency price movements, which can be assumed as noise. Then, the HMM is used to forecast the probability distribution of the price return accumulated over the next F days. Finally, from this distribution, we infer future price trends. Our results indicate that the proposed methodology might be a useful decision support tool for agents participating in the crude oil market. (author)

  18. Forecasting Electricity Spot Prices Accounting for Wind Power Predictions

    DEFF Research Database (Denmark)

    Jónsson, Tryggvi; Pinson, Pierre; Nielsen, Henrik Aalborg

    2013-01-01

    A two-step methodology for forecasting of electricity spot prices is introduced, with focus on the impact of predicted system load and wind power generation. The nonlinear and nonstationary influence of these explanatory variables is accommodated in a first step based on a nonparametric and time...

  19. Price forecasting of day-ahead electricity markets using a hybrid forecast method

    International Nuclear Information System (INIS)

    Shafie-khah, M.; Moghaddam, M. Parsa; Sheikh-El-Eslami, M.K.

    2011-01-01

    Research highlights: → A hybrid method is proposed to forecast the day-ahead prices in electricity market. → The method combines Wavelet-ARIMA and RBFN network models. → PSO method is applied to obtain optimum RBFN structure for avoiding over fitting. → One of the merits of the proposed method is lower need to the input data. → The proposed method has more accurate behavior in compare with previous methods. -- Abstract: Energy price forecasting in a competitive electricity market is crucial for the market participants in planning their operations and managing their risk, and it is also the key information in the economic optimization of the electric power industry. However, price series usually have a complex behavior due to their nonlinearity, nonstationarity, and time variancy. In this paper, a novel hybrid method to forecast day-ahead electricity price is proposed. This hybrid method is based on wavelet transform, Auto-Regressive Integrated Moving Average (ARIMA) models and Radial Basis Function Neural Networks (RBFN). The wavelet transform provides a set of better-behaved constitutive series than price series for prediction. ARIMA model is used to generate a linear forecast, and then RBFN is developed as a tool for nonlinear pattern recognition to correct the estimation error in wavelet-ARIMA forecast. Particle Swarm Optimization (PSO) is used to optimize the network structure which makes the RBFN be adapted to the specified training set, reducing computation complexity and avoiding overfitting. The proposed method is examined on the electricity market of mainland Spain and the results are compared with some of the most recent price forecast methods. The results show that the proposed hybrid method could provide a considerable improvement for the forecasting accuracy.

  20. Price forecasting of day-ahead electricity markets using a hybrid forecast method

    Energy Technology Data Exchange (ETDEWEB)

    Shafie-khah, M., E-mail: miadreza@gmail.co [Tarbiat Modares University, Tehran (Iran, Islamic Republic of); Moghaddam, M. Parsa, E-mail: parsa@modares.ac.i [Tarbiat Modares University, Tehran (Iran, Islamic Republic of); Sheikh-El-Eslami, M.K., E-mail: aleslam@modares.ac.i [Tarbiat Modares University, Tehran (Iran, Islamic Republic of)

    2011-05-15

    Research highlights: {yields} A hybrid method is proposed to forecast the day-ahead prices in electricity market. {yields} The method combines Wavelet-ARIMA and RBFN network models. {yields} PSO method is applied to obtain optimum RBFN structure for avoiding over fitting. {yields} One of the merits of the proposed method is lower need to the input data. {yields} The proposed method has more accurate behavior in compare with previous methods. -- Abstract: Energy price forecasting in a competitive electricity market is crucial for the market participants in planning their operations and managing their risk, and it is also the key information in the economic optimization of the electric power industry. However, price series usually have a complex behavior due to their nonlinearity, nonstationarity, and time variancy. In this paper, a novel hybrid method to forecast day-ahead electricity price is proposed. This hybrid method is based on wavelet transform, Auto-Regressive Integrated Moving Average (ARIMA) models and Radial Basis Function Neural Networks (RBFN). The wavelet transform provides a set of better-behaved constitutive series than price series for prediction. ARIMA model is used to generate a linear forecast, and then RBFN is developed as a tool for nonlinear pattern recognition to correct the estimation error in wavelet-ARIMA forecast. Particle Swarm Optimization (PSO) is used to optimize the network structure which makes the RBFN be adapted to the specified training set, reducing computation complexity and avoiding overfitting. The proposed method is examined on the electricity market of mainland Spain and the results are compared with some of the most recent price forecast methods. The results show that the proposed hybrid method could provide a considerable improvement for the forecasting accuracy.

  1. Electricity demand and spot price forecasting using evolutionary computation combined with chaotic nonlinear dynamic model

    International Nuclear Information System (INIS)

    Unsihuay-Vila, C.; Zambroni de Souza, A.C.; Marangon-Lima, J.W.; Balestrassi, P.P.

    2010-01-01

    This paper proposes a new hybrid approach based on nonlinear chaotic dynamics and evolutionary strategy to forecast electricity loads and prices. The main idea is to develop a new training or identification stage in a nonlinear chaotic dynamic based predictor. In the training stage five optimal parameters for a chaotic based predictor are searched through an optimization model based on evolutionary strategy. The objective function of the optimization model is the mismatch minimization between the multi-step-ahead forecasting of predictor and observed data such as it is done in identification problems. The first contribution of this paper is that the proposed approach is capable of capturing the complex dynamic of demand and price time series considered resulting in a more accuracy forecasting. The second contribution is that the proposed approach run on-line manner, i.e. the optimal set of parameters and prediction is executed automatically which can be used to prediction in real-time, it is an advantage in comparison with other models, where the choice of their input parameters are carried out off-line, following qualitative/experience-based recipes. A case study of load and price forecasting is presented using data from New England, Alberta, and Spain. A comparison with other methods such as autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) is shown. The results show that the proposed approach provides a more accurate and effective forecasting than ARIMA and ANN methods. (author)

  2. Forecasting European thermal coal spot prices

    Directory of Open Access Journals (Sweden)

    Alicja Krzemień

    2015-01-01

    Finally, in order to analyse the time series model performance a Generalized Regression Neural Network (GRNN was used and its performance compared against the whole AR(2 process. Empirical results obtained confirmed that there is no statistically significant difference between both methods. The GRNN analysis also allowed pointing out the main drivers that move the European Thermal Coal Spot prices: crude oil, USD/CNY change and supply side drivers.

  3. Issues in midterm analysis and forecasting 1998

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1998-07-01

    Issues in Midterm Analysis and Forecasting 1998 (Issues) presents a series of nine papers covering topics in analysis and modeling that underlie the Annual Energy Outlook 1998 (AEO98), as well as other significant issues in midterm energy markets. AEO98, DOE/EIA-0383(98), published in December 1997, presents national forecasts of energy production, demand, imports, and prices through the year 2020 for five cases -- a reference case and four additional cases that assume higher and lower economic growth and higher and lower world oil prices than in the reference case. The forecasts were prepared by the Energy Information Administration (EIA), using EIA`s National Energy Modeling System (NEMS). The papers included in Issues describe underlying analyses for the projections in AEO98 and the forthcoming Annual Energy Outlook 1999 and for other products of EIA`s Office of Integrated Analysis and Forecasting. Their purpose is to provide public access to analytical work done in preparation for the midterm projections and other unpublished analyses. Specific topics were chosen for their relevance to current energy issues or to highlight modeling activities in NEMS. 59 figs., 44 tabs.

  4. Stock price forecasting based on time series analysis

    Science.gov (United States)

    Chi, Wan Le

    2018-05-01

    Using the historical stock price data to set up a sequence model to explain the intrinsic relationship of data, the future stock price can forecasted. The used models are auto-regressive model, moving-average model and autoregressive-movingaverage model. The original data sequence of unit root test was used to judge whether the original data sequence was stationary. The non-stationary original sequence as a first order difference needed further processing. Then the stability of the sequence difference was re-inspected. If it is still non-stationary, the second order differential processing of the sequence is carried out. Autocorrelation diagram and partial correlation diagram were used to evaluate the parameters of the identified ARMA model, including coefficients of the model and model order. Finally, the model was used to forecast the fitting of the shanghai composite index daily closing price with precision. Results showed that the non-stationary original data series was stationary after the second order difference. The forecast value of shanghai composite index daily closing price was closer to actual value, indicating that the ARMA model in the paper was a certain accuracy.

  5. Short-Term Price Forecasting Models Based on Artificial Neural Networks for Intraday Sessions in the Iberian Electricity Market

    Directory of Open Access Journals (Sweden)

    Claudio Monteiro

    2016-09-01

    Full Text Available This paper presents novel intraday session models for price forecasts (ISMPF models for hourly price forecasting in the six intraday sessions of the Iberian electricity market (MIBEL and the analysis of mean absolute percentage errors (MAPEs obtained with suitable combinations of their input variables in order to find the best ISMPF models. Comparisons of errors from different ISMPF models identified the most important variables for forecasting purposes. Similar analyses were applied to determine the best daily session models for price forecasts (DSMPF models for the day-ahead price forecasting in the daily session of the MIBEL, considering as input variables extensive hourly time series records of recent prices, power demands and power generations in the previous day, forecasts of demand, wind power generation and weather for the day-ahead, and chronological variables. ISMPF models include the input variables of DSMPF models as well as the daily session prices and prices of preceding intraday sessions. The best ISMPF models achieved lower MAPEs for most of the intraday sessions compared to the error of the best DSMPF model; furthermore, such DSMPF error was very close to the lowest limit error for the daily session. The best ISMPF models can be useful for MIBEL agents of the electricity intraday market and the electric energy industry.

  6. Wavelet regression model in forecasting crude oil price

    Science.gov (United States)

    Hamid, Mohd Helmie; Shabri, Ani

    2017-05-01

    This study presents the performance of wavelet multiple linear regression (WMLR) technique in daily crude oil forecasting. WMLR model was developed by integrating the discrete wavelet transform (DWT) and multiple linear regression (MLR) model. The original time series was decomposed to sub-time series with different scales by wavelet theory. Correlation analysis was conducted to assist in the selection of optimal decomposed components as inputs for the WMLR model. The daily WTI crude oil price series has been used in this study to test the prediction capability of the proposed model. The forecasting performance of WMLR model were also compared with regular multiple linear regression (MLR), Autoregressive Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) using root mean square errors (RMSE) and mean absolute errors (MAE). Based on the experimental results, it appears that the WMLR model performs better than the other forecasting technique tested in this study.

  7. Electricity prices forecasting by automatic dynamic harmonic regression models

    International Nuclear Information System (INIS)

    Pedregal, Diego J.; Trapero, Juan R.

    2007-01-01

    The changes experienced by electricity markets in recent years have created the necessity for more accurate forecast tools of electricity prices, both for producers and consumers. Many methodologies have been applied to this aim, but in the view of the authors, state space models are not yet fully exploited. The present paper proposes a univariate dynamic harmonic regression model set up in a state space framework for forecasting prices in these markets. The advantages of the approach are threefold. Firstly, a fast automatic identification and estimation procedure is proposed based on the frequency domain. Secondly, the recursive algorithms applied offer adaptive predictions that compare favourably with respect to other techniques. Finally, since the method is based on unobserved components models, explicit information about trend, seasonal and irregular behaviours of the series can be extracted. This information is of great value to the electricity companies' managers in order to improve their strategies, i.e. it provides management innovations. The good forecast performance and the rapid adaptability of the model to changes in the data are illustrated with actual prices taken from the PJM interconnection in the US and for the Spanish market for the year 2002. (author)

  8. Short-term electricity prices forecasting in a competitive market by a hybrid intelligent approach

    Energy Technology Data Exchange (ETDEWEB)

    Catalao, J.P.S. [Department of Electromechanical Engineering, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilha (Portugal); Center for Innovation in Electrical and Energy Engineering, Instituto Superior Tecnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001 Lisbon (Portugal); Pousinho, H.M.I. [Department of Electromechanical Engineering, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilha (Portugal); Mendes, V.M.F. [Department of Electrical Engineering and Automation, Instituto Superior de Engenharia de Lisboa, R. Conselheiro Emidio Navarro, 1950-062 Lisbon (Portugal)

    2011-02-15

    In this paper, a hybrid intelligent approach is proposed for short-term electricity prices forecasting in a competitive market. The proposed approach is based on the wavelet transform and a hybrid of neural networks and fuzzy logic. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Conclusions are duly drawn. (author)

  9. Short-term electricity prices forecasting in a competitive market by a hybrid intelligent approach

    International Nuclear Information System (INIS)

    Catalao, J.P.S.; Pousinho, H.M.I.; Mendes, V.M.F.

    2011-01-01

    In this paper, a hybrid intelligent approach is proposed for short-term electricity prices forecasting in a competitive market. The proposed approach is based on the wavelet transform and a hybrid of neural networks and fuzzy logic. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Conclusions are duly drawn. (author)

  10. Adaptive short-term electricity price forecasting using artificial neural networks in the restructured power markets

    International Nuclear Information System (INIS)

    Yamin, H.Y.; Shahidehpour, S.M.; Li, Z.

    2004-01-01

    This paper proposes a comprehensive model for the adaptive short-term electricity price forecasting using Artificial Neural Networks (ANN) in the restructured power markets. The model consists: price simulation, price forecasting, and performance analysis. The factors impacting the electricity price forecasting, including time factors, load factors, reserve factors, and historical price factor are discussed. We adopted ANN and proposed a new definition for the MAPE using the median to study the relationship between these factors and market price as well as the performance of the electricity price forecasting. The reserve factors are included to enhance the performance of the forecasting process. The proposed model handles the price spikes more efficiently due to considering the median instead of the average. The IEEE 118-bus system and California practical system are used to demonstrate the superiority of the proposed model. (author)

  11. Deterministic Echo State Networks Based Stock Price Forecasting

    Directory of Open Access Journals (Sweden)

    Jingpei Dan

    2014-01-01

    Full Text Available Echo state networks (ESNs, as efficient and powerful computational models for approximating nonlinear dynamical systems, have been successfully applied in financial time series forecasting. Reservoir constructions in standard ESNs rely on trials and errors in real applications due to a series of randomized model building stages. A novel form of ESN with deterministically constructed reservoir is competitive with standard ESN by minimal complexity and possibility of optimizations for ESN specifications. In this paper, forecasting performances of deterministic ESNs are investigated in stock price prediction applications. The experiment results on two benchmark datasets (Shanghai Composite Index and S&P500 demonstrate that deterministic ESNs outperform standard ESN in both accuracy and efficiency, which indicate the prospect of deterministic ESNs for financial prediction.

  12. Price Density Forecasts in the U.S. Hog Market: Composite Procedures

    NARCIS (Netherlands)

    Trujillo Barrera, A.A.; Garcia, P.; Mallory, M.

    2013-01-01

    Abstract We develop and evaluate quarterly out-of-sample individual and composite density forecasts for U.S. hog prices using data from 1975.I to 2010.IV. Individual forecasts are generated from time series models and the implied distribution of USDA outlook forecasts. Composite density forecasts

  13. Palm oil price forecasting model: An autoregressive distributed lag (ARDL) approach

    Science.gov (United States)

    Hamid, Mohd Fahmi Abdul; Shabri, Ani

    2017-05-01

    Palm oil price fluctuated without any clear trend or cyclical pattern in the last few decades. The instability of food commodities price causes it to change rapidly over time. This paper attempts to develop Autoregressive Distributed Lag (ARDL) model in modeling and forecasting the price of palm oil. In order to use ARDL as a forecasting model, this paper modifies the data structure where we only consider lagged explanatory variables to explain the variation in palm oil price. We then compare the performance of this ARDL model with a benchmark model namely ARIMA in term of their comparative forecasting accuracy. This paper also utilize ARDL bound testing approach to co-integration in examining the short run and long run relationship between palm oil price and its determinant; production, stock, and price of soybean as the substitute of palm oil and price of crude oil. The comparative forecasting accuracy suggests that ARDL model has a better forecasting accuracy compared to ARIMA.

  14. Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models

    International Nuclear Information System (INIS)

    Tan, Zhongfu; Zhang, Jinliang; Xu, Jun; Wang, Jianhui

    2010-01-01

    This paper proposes a novel price forecasting method based on wavelet transform combined with ARIMA and GARCH models. By wavelet transform, the historical price series is decomposed and reconstructed into one approximation series and some detail series. Then each subseries can be separately predicted by a suitable time series model. The final forecast is obtained by composing the forecasted results of each subseries. This proposed method is examined on Spanish and PJM electricity markets and compared with some other forecasting methods. (author)

  15. Forecasting oil price movements with crack spread futures

    International Nuclear Information System (INIS)

    Murat, Atilim; Tokat, Ekin

    2009-01-01

    In oil markets, the crack spread refers to the crude-product price relationship. Refiners are major participants in oil markets and they are primarily exposed to the crack spread. In other words, refiner activity is substantially driven by the objective of protecting the crack spread. Moreover, oil consumers are active participants in the oil hedging market and they are frequently exposed to the crack spread. From another perspective, hedge funds are heavily using crack spread to speculate in oil markets. Based on the high volume of crack spread futures trading in oil markets, the question we want to raise is whether the crack spread futures can be a good predictor of oil price movements. We investigated first whether there is a causal relationship between the crack spread futures and the spot oil markets in a vector error correction framework. We found the causal impact of crack spread futures on spot oil market both in the long- and the short-run after April 2003 where we detected a structural break in the model. To examine the forecasting performance, we use the random walk model (RWM) as a benchmark, and we also evaluate the forecasting power of crack spread futures against the crude oil futures. The results showed that (a) both the crack spread futures and the crude oil futures outperformed the RWM; and (b) the crack spread futures are almost as good as the crude oil futures in predicting the movements in spot oil markets. (author)

  16. Modeling and forecasting electricity price jumps in the Nord Pool power market

    DEFF Research Database (Denmark)

    Knapik, Oskar

    extreme prices and forecasting of the price jumps is crucial for risk management and market design. In this paper, we consider the problem of the impact of fundamental price drivers on forecasting of price jumps in NordPool intraday market. We develop categorical time series models which take into account......For risk management traders in the electricity market are mainly interested in the risk of negative (drops) or of positive (spikes) price jumps, i.e. the sellers face the risk of negative price jumps while the buyers face the risk of positive price jumps. Understanding the mechanism that drive...

  17. Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection

    DEFF Research Database (Denmark)

    Bork, Lasse; Møller, Stig Vinther

    2015-01-01

    We examine house price forecastability across the 50 states using Dynamic Model Averaging and Dynamic Model Selection, which allow for model change and parameter shifts. By allowing the entire forecasting model to change over time and across locations, the forecasting accuracy improves substantia......We examine house price forecastability across the 50 states using Dynamic Model Averaging and Dynamic Model Selection, which allow for model change and parameter shifts. By allowing the entire forecasting model to change over time and across locations, the forecasting accuracy improves...

  18. Oil prices. Brownian motion or mean reversion? A study using a one year ahead density forecast criterion

    International Nuclear Information System (INIS)

    Meade, Nigel

    2010-01-01

    For oil related investment appraisal, an accurate description of the evolving uncertainty in the oil price is essential. For example, when using real option theory to value an investment, a density function for the future price of oil is central to the option valuation. The literature on oil pricing offers two views. The arbitrage pricing theory literature for oil suggests geometric Brownian motion and mean reversion models. Empirically driven literature suggests ARMA-GARCH models. In addition to reflecting the volatility of the market, the density function of future prices should also incorporate the uncertainty due to price jumps, a common occurrence in the oil market. In this study, the accuracy of density forecasts for up to a year ahead is the major criterion for a comparison of a range of models of oil price behaviour, both those proposed in the literature and following from data analysis. The Kullbach Leibler information criterion is used to measure the accuracy of density forecasts. Using two crude oil price series, Brent and West Texas Intermediate (WTI) representing the US market, we demonstrate that accurate density forecasts are achievable for up to nearly two years ahead using a mixture of two Gaussians innovation processes with GARCH and no mean reversion. (author)

  19. Oil prices. Brownian motion or mean reversion? A study using a one year ahead density forecast criterion

    Energy Technology Data Exchange (ETDEWEB)

    Meade, Nigel [Imperial College, Business School London (United Kingdom)

    2010-11-15

    For oil related investment appraisal, an accurate description of the evolving uncertainty in the oil price is essential. For example, when using real option theory to value an investment, a density function for the future price of oil is central to the option valuation. The literature on oil pricing offers two views. The arbitrage pricing theory literature for oil suggests geometric Brownian motion and mean reversion models. Empirically driven literature suggests ARMA-GARCH models. In addition to reflecting the volatility of the market, the density function of future prices should also incorporate the uncertainty due to price jumps, a common occurrence in the oil market. In this study, the accuracy of density forecasts for up to a year ahead is the major criterion for a comparison of a range of models of oil price behaviour, both those proposed in the literature and following from data analysis. The Kullbach Leibler information criterion is used to measure the accuracy of density forecasts. Using two crude oil price series, Brent and West Texas Intermediate (WTI) representing the US market, we demonstrate that accurate density forecasts are achievable for up to nearly two years ahead using a mixture of two Gaussians innovation processes with GARCH and no mean reversion. (author)

  20. Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets

    OpenAIRE

    Ziel, Florian; Steinert, Rick; Husmann, Sven

    2015-01-01

    In our paper we analyze the relationship between the day-ahead electricity price of the Energy Exchange Austria (EXAA) and other day-ahead electricity prices in Europe. We focus on markets, which settle their prices after the EXAA, which enables traders to include the EXAA price into their calculations. For each market we employ econometric models to incorporate the EXAA price and compare them with their counterparts without the price of the Austrian exchange. By employing a forecasting study...

  1. Neural networks approach to forecast several hour ahead electricity prices and loads in deregulated market

    Energy Technology Data Exchange (ETDEWEB)

    Mandal, Paras; Senjyu, Tomonobu [Department of Electrical and Electronics, University of the Ryukyus, 1 Senbaru, Nagakami Nishihara, Okinawa 903-0213 (Japan); Funabashi, Toshihisa [Meidensha Corporation, Tokyo 103-8515 (Japan)

    2006-09-15

    In daily power markets, forecasting electricity prices and loads are the most essential task and the basis for any decision making. An approach to predict the market behaviors is to use the historical prices, loads and other required information to forecast the future prices and loads. This paper introduces an approach for several hour ahead (1-6h) electricity price and load forecasting using an artificial intelligence method, such as a neural network model, which uses publicly available data from the NEMMCO web site to forecast electricity prices and loads for the Victorian electricity market. An approach of selection of similar days is proposed according to which the load and price curves are forecasted by using the information of the days being similar to that of the forecast day. A Euclidean norm with weighted factors is used for the selection of the similar days. Two different ANN models, one for one to six hour ahead load forecasting and another for one to six hour ahead price forecasting have been proposed. The MAPE (mean absolute percentage error) results show a clear increasing trend with the increase in hour ahead load and price forecasting. The sample average of MAPEs for one hour ahead price forecasts is 9.75%. This figure increases to only 20.03% for six hour ahead predictions. Similarly, the one to six hour ahead load forecast errors (MAPE) range from 0.56% to 1.30% only. MAPE results show that several hour ahead electricity prices and loads in the deregulated Victorian market can be forecasted with reasonable accuracy. (author)

  2. Neural networks approach to forecast several hour ahead electricity prices and loads in deregulated market

    International Nuclear Information System (INIS)

    Mandal, Paras; Senjyu, Tomonobu; Funabashi, Toshihisa

    2006-01-01

    In daily power markets, forecasting electricity prices and loads are the most essential task and the basis for any decision making. An approach to predict the market behaviors is to use the historical prices, loads and other required information to forecast the future prices and loads. This paper introduces an approach for several hour ahead (1-6 h) electricity price and load forecasting using an artificial intelligence method, such as a neural network model, which uses publicly available data from the NEMMCO web site to forecast electricity prices and loads for the Victorian electricity market. An approach of selection of similar days is proposed according to which the load and price curves are forecasted by using the information of the days being similar to that of the forecast day. A Euclidean norm with weighted factors is used for the selection of the similar days. Two different ANN models, one for one to six hour ahead load forecasting and another for one to six hour ahead price forecasting have been proposed. The MAPE (mean absolute percentage error) results show a clear increasing trend with the increase in hour ahead load and price forecasting. The sample average of MAPEs for one hour ahead price forecasts is 9.75%. This figure increases to only 20.03% for six hour ahead predictions. Similarly, the one to six hour ahead load forecast errors (MAPE) range from 0.56% to 1.30% only. MAPE results show that several hour ahead electricity prices and loads in the deregulated Victorian market can be forecasted with reasonable accuracy

  3. Mixed price and load forecasting of electricity markets by a new iterative prediction method

    International Nuclear Information System (INIS)

    Amjady, Nima; Daraeepour, Ali

    2009-01-01

    Load and price forecasting are the two key issues for the participants of current electricity markets. However, load and price of electricity markets have complex characteristics such as nonlinearity, non-stationarity and multiple seasonality, to name a few (usually, more volatility is seen in the behavior of electricity price signal). For these reasons, much research has been devoted to load and price forecast, especially in the recent years. However, previous research works in the area separately predict load and price signals. In this paper, a mixed model for load and price forecasting is presented, which can consider interactions of these two forecast processes. The mixed model is based on an iterative neural network based prediction technique. It is shown that the proposed model can present lower forecast errors for both load and price compared with the previous separate frameworks. Another advantage of the mixed model is that all required forecast features (from load or price) are predicted within the model without assuming known values for these features. So, the proposed model can better be adapted to real conditions of an electricity market. The forecast accuracy of the proposed mixed method is evaluated by means of real data from the New York and Spanish electricity markets. The method is also compared with some of the most recent load and price forecast techniques. (author)

  4. The long-run forecasting of energy prices using the model of shifting trend

    International Nuclear Information System (INIS)

    Radchenko, Stanislav

    2005-01-01

    Developing models for accurate long-term energy price forecasting is an important problem because these forecasts should be useful in determining both supply and demand of energy. On the supply side, long-term forecasts determine investment decisions of energy-related companies. On the demand side, investments in physical capital and durable goods depend on price forecasts of a particular energy type. Forecasting long-run rend movements in energy prices is very important on the macroeconomic level for several developing countries because energy prices have large impacts on their real output, the balance of payments, fiscal policy, etc. Pindyck (1999) argues that the dynamics of real energy prices is mean-reverting to trend lines with slopes and levels that are shifting unpredictably over time. The hypothesis of shifting long-term trend lines was statistically tested by Benard et al. (2004). The authors find statistically significant instabilities for coal and natural gas prices. I continue the research of energy prices in the framework of continuously shifting levels and slopes of trend lines started by Pindyck (1999). The examined model offers both parsimonious approach and perspective on the developments in energy markets. Using the model of depletable resource production, Pindyck (1999) argued that the forecast of energy prices in the model is based on the long-run total marginal cost. Because the model of a shifting trend is based on the competitive behavior, one may examine deviations of oil producers from the competitive behavior by studying the difference between actual prices and long-term forecasts. To construct the long-run forecasts (10-year-ahead and 15-year-ahead) of energy prices, I modify the univariate shifting trends model of Pindyck (1999). I relax some assumptions on model parameters, the assumption of white noise error term, and propose a new Bayesian approach utilizing a Gibbs sampling algorithm to estimate the model with autocorrelation. To

  5. A Note on Forecasting the Rate of Change of the Price of Oil: Asymmetric Loss and Forecast Rationality

    Directory of Open Access Journals (Sweden)

    Christian Pierdzioch

    2013-03-01

    Full Text Available We study whether forecasts of the rate of change of the price of oil are rational. To this end, we consider a model that allows the shape of forecasters’ loss function to be studied. The shape of forecasters’ loss function may be consistent with a symmetric or an asymmetric loss function. We find that an asymmetric loss function often (but not always makes forecasts look rational, and we also report that forecast rationality may have changed over time.

  6. Fünf Jahre Zugelassener Wirtschaftsbeteiligter (AEO)

    OpenAIRE

    Weerth, Carsten

    2014-01-01

    Der Zugelassene Wirtschaftsbeteiligte (AEO) wurde mit Wirkung vom 1. Januar 2008 geschaffen. Vier Jahre sind vergangen, nach denen es nun erneut gilt, eine Zwischenbilanz zu ziehen – wurden die Erwartungen und Antragszahlen erreicht, was gibt es an Neuerungen und welche Tendenzen sind erkennbar? Neue rechtliche Rahmenbedingungen gelten bereits seit dem 1. Januar 2009 – die Kriterien für alle zollrechtlichen Bewilligungen von vereinfachten Anmeldeverfahren und Anschreibeverfahren (Einfuhr und ...

  7. CAViaR-based forecast for oil price risk

    International Nuclear Information System (INIS)

    Huang, Dashan; Yu, Baimin; Fabozzi, Frank J.; Fukushima, Masao

    2009-01-01

    As a benchmark for measuring market risk, value-at-risk (VaR) reduces the risk associated with any kind of asset to just a number (amount in terms of a currency), which can be well understood by regulators, board members, and other interested parties. This paper employs a new VaR approach due to Engle and Manganelli [Engle, R.F., Manganelli, S., 2004. CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles. Journal of Business and Economic Statistics 22, 367-381] to forecasting oil price risk. In doing so, we provide two original contributions by introducing a new exponentially weighted moving average CAViaR model and developing a mixed data regression model for multi-period VaR prediction. (author)

  8. Dynamical behavior of price forecasting in structures of group correlations

    Science.gov (United States)

    Lim, Kyuseong; Kim, Soo Yong; Kim, Kyungsik

    2015-07-01

    We investigate the prediction of the future prices from the structures and the networks of the companies in special financial groups. After the financial group network has been constructed from the value of the high cross-correlation, each company in a group is simulated and analyzed how it buys or sells stock is anaylzed and how it makes rational investments is forecasted. In the shortmemory behavior rather than the long-memory behavior, each company among a group can make a rational investment decision by using a stochastic evolution rule in the financial network. In particular, we simulate and analyze the investment situation in connection with the empirical data and the simulated result.

  9. Daily Crude Oil Price Forecasting Using Hybridizing Wavelet and Artificial Neural Network Model

    Directory of Open Access Journals (Sweden)

    Ani Shabri

    2014-01-01

    Full Text Available A new method based on integrating discrete wavelet transform and artificial neural networks (WANN model for daily crude oil price forecasting is proposed. The discrete Mallat wavelet transform is used to decompose the crude price series into one approximation series and some details series (DS. The new series obtained by adding the effective one approximation series and DS component is then used as input into the ANN model to forecast crude oil price. The relative performance of WANN model was compared to regular ANN model for crude oil forecasting at lead times of 1 day for two main crude oil price series, West Texas Intermediate (WTI and Brent crude oil spot prices. In both cases, WANN model was found to provide more accurate crude oil prices forecasts than individual ANN model.

  10. Oil price assumptions in macroeconomic forecasts: should we follow future market expectations?

    International Nuclear Information System (INIS)

    Coimbra, C.; Esteves, P.S.

    2004-01-01

    In macroeconomic forecasting, in spite of its important role in price and activity developments, oil prices are usually taken as an exogenous variable, for which assumptions have to be made. This paper evaluates the forecasting performance of futures market prices against the other popular technical procedure, the carry-over assumption. The results suggest that there is almost no difference between opting for futures market prices or using the carry-over assumption for short-term forecasting horizons (up to 12 months), while, for longer-term horizons, they favour the use of futures market prices. However, as futures market prices reflect market expectations for world economic activity, futures oil prices should be adjusted whenever market expectations for world economic growth are different to the values underlying the macroeconomic scenarios, in order to fully ensure the internal consistency of those scenarios. (Author)

  11. Probabilistic electricity price forecasting with variational heteroscedastic Gaussian process and active learning

    International Nuclear Information System (INIS)

    Kou, Peng; Liang, Deliang; Gao, Lin; Lou, Jianyong

    2015-01-01

    Highlights: • A novel active learning model for the probabilistic electricity price forecasting. • Heteroscedastic Gaussian process that captures the local volatility of the electricity price. • Variational Bayesian learning that avoids over-fitting. • Active learning algorithm that reduces the computational efforts. - Abstract: Electricity price forecasting is essential for the market participants in their decision making. Nevertheless, the accuracy of such forecasting cannot be guaranteed due to the high variability of the price data. For this reason, in many cases, rather than merely point forecasting results, market participants are more interested in the probabilistic price forecasting results, i.e., the prediction intervals of the electricity price. Focusing on this issue, this paper proposes a new model for the probabilistic electricity price forecasting. This model is based on the active learning technique and the variational heteroscedastic Gaussian process (VHGP). It provides the heteroscedastic Gaussian prediction intervals, which effectively quantify the heteroscedastic uncertainties associated with the price data. Because the high computational effort of VHGP hinders its application to the large-scale electricity price forecasting tasks, we design an active learning algorithm to select a most informative training subset from the whole available training set. By constructing the forecasting model on this smaller subset, the computational efforts can be significantly reduced. In this way, the practical applicability of the proposed model is enhanced. The forecasting performance and the computational time of the proposed model are evaluated using the real-world electricity price data, which is obtained from the ANEM, PJM, and New England ISO

  12. Forecasting Natural Gas Prices Using Wavelets, Time Series, and Artificial Neural Networks.

    Science.gov (United States)

    Jin, Junghwan; Kim, Jinsoo

    2015-01-01

    Following the unconventional gas revolution, the forecasting of natural gas prices has become increasingly important because the association of these prices with those of crude oil has weakened. With this as motivation, we propose some modified hybrid models in which various combinations of the wavelet approximation, detail components, autoregressive integrated moving average, generalized autoregressive conditional heteroskedasticity, and artificial neural network models are employed to predict natural gas prices. We also emphasize the boundary problem in wavelet decomposition, and compare results that consider the boundary problem case with those that do not. The empirical results show that our suggested approach can handle the boundary problem, such that it facilitates the extraction of the appropriate forecasting results. The performance of the wavelet-hybrid approach was superior in all cases, whereas the application of detail components in the forecasting was only able to yield a small improvement in forecasting performance. Therefore, forecasting with only an approximation component would be acceptable, in consideration of forecasting efficiency.

  13. Advances in electric power and energy systems load and price forecasting

    CERN Document Server

    2017-01-01

    A comprehensive review of state-of-the-art approaches to power systems forecasting from the most respected names in the field, internationally. Advances in Electric Power and Energy Systems is the first book devoted exclusively to a subject of increasing urgency to power systems planning and operations. Written for practicing engineers, researchers, and post-grads concerned with power systems planning and forecasting, this book brings together contributions from many of the world’s foremost names in the field who address a range of critical issues, from forecasting power system load to power system pricing to post-storm service restoration times, river flow forecasting, and more. In a time of ever-increasing energy demands, mounting concerns over the environmental impacts of power generation, and the emergence of new, smart-grid technologies, electricity price forecasting has assumed a prominent role within both the academic and industrial ar nas. Short-run forecasting of electricity prices has become nece...

  14. AN EVALUATION OF POINT AND DENSITY FORECASTS FOR SELECTED EU FARM GATE MILK PRICES

    Directory of Open Access Journals (Sweden)

    Dennis Bergmann

    2018-01-01

    Full Text Available Fundamental changes to the common agricultural policy (CAP have led to greater market orientation which in turn has resulted in sharply increased variability of EU farm gate milk prices and thus farmers’ income. In this market environment reliable forecasts of farm gate milk prices are extremely important as farmers can make improved decisions with regards to cash flow management and budget preparation. In addition these forecasts may be used in setting fixed priced contracts between dairy farmers and processors thus providing certainty and reducing risk. In this study both point and density forecasts from various time series models for farm gate milk prices in Germany, Ireland and for an average EU price series are evaluated using a rolling window framework. Additionally forecasts of the individual models are combined using different combination schemes. The results of the out of sample evaluation show that ARIMA type models perform well on short forecast horizons (1 to 3 month while the structural time series approach performs well on longer forecast horizons (12 month. Finally combining individual forecasts of different models significantly improves the forecast performance for all forecast horizons.

  15. Short-term electricity prices forecasting in a competitive market: A neural network approach

    International Nuclear Information System (INIS)

    Catalao, J.P.S.; Mariano, S.J.P.S.; Mendes, V.M.F.; Ferreira, L.A.F.M.

    2007-01-01

    This paper proposes a neural network approach for forecasting short-term electricity prices. Almost until the end of last century, electricity supply was considered a public service and any price forecasting which was undertaken tended to be over the longer term, concerning future fuel prices and technical improvements. Nowadays, short-term forecasts have become increasingly important since the rise of the competitive electricity markets. In this new competitive framework, short-term price forecasting is required by producers and consumers to derive their bidding strategies to the electricity market. Accurate forecasting tools are essential for producers to maximize their profits, avowing profit losses over the misjudgement of future price movements, and for consumers to maximize their utilities. A three-layered feedforward neural network, trained by the Levenberg-Marquardt algorithm, is used for forecasting next-week electricity prices. We evaluate the accuracy of the price forecasting attained with the proposed neural network approach, reporting the results from the electricity markets of mainland Spain and California. (author)

  16. Probabilistic Price Forecasting for Day-Ahead and Intraday Markets: Beyond the Statistical Model

    Directory of Open Access Journals (Sweden)

    José R. Andrade

    2017-10-01

    Full Text Available Forecasting the hourly spot price of day-ahead and intraday markets is particularly challenging in electric power systems characterized by high installed capacity of renewable energy technologies. In particular, periods with low and high price levels are difficult to predict due to a limited number of representative cases in the historical dataset, which leads to forecast bias problems and wide forecast intervals. Moreover, these markets also require the inclusion of multiple explanatory variables, which increases the complexity of the model without guaranteeing a forecasting skill improvement. This paper explores information from daily futures contract trading and forecast of the daily average spot price to correct point and probabilistic forecasting bias. It also shows that an adequate choice of explanatory variables and use of simple models like linear quantile regression can lead to highly accurate spot price point and probabilistic forecasts. In terms of point forecast, the mean absolute error was 3.03 €/MWh for day-ahead market and a maximum value of 2.53 €/MWh was obtained for intraday session 6. The probabilistic forecast results show sharp forecast intervals and deviations from perfect calibration below 7% for all market sessions.

  17. Forecasting electricity spot-prices using linear univariate time-series models

    International Nuclear Information System (INIS)

    Cuaresma, Jesus Crespo; Hlouskova, Jaroslava; Kossmeier, Stephan; Obersteiner, Michael

    2004-01-01

    This paper studies the forecasting abilities of a battery of univariate models on hourly electricity spot prices, using data from the Leipzig Power Exchange. The specifications studied include autoregressive models, autoregressive-moving average models and unobserved component models. The results show that specifications, where each hour of the day is modelled separately present uniformly better forecasting properties than specifications for the whole time-series, and that the inclusion of simple probabilistic processes for the arrival of extreme price events can lead to improvements in the forecasting abilities of univariate models for electricity spot prices. (Author)

  18. Crude oil price analysis and forecasting based on variational mode decomposition and independent component analysis

    Science.gov (United States)

    E, Jianwei; Bao, Yanling; Ye, Jimin

    2017-10-01

    As one of the most vital energy resources in the world, crude oil plays a significant role in international economic market. The fluctuation of crude oil price has attracted academic and commercial attention. There exist many methods in forecasting the trend of crude oil price. However, traditional models failed in predicting accurately. Based on this, a hybrid method will be proposed in this paper, which combines variational mode decomposition (VMD), independent component analysis (ICA) and autoregressive integrated moving average (ARIMA), called VMD-ICA-ARIMA. The purpose of this study is to analyze the influence factors of crude oil price and predict the future crude oil price. Major steps can be concluded as follows: Firstly, applying the VMD model on the original signal (crude oil price), the modes function can be decomposed adaptively. Secondly, independent components are separated by the ICA, and how the independent components affect the crude oil price is analyzed. Finally, forecasting the price of crude oil price by the ARIMA model, the forecasting trend demonstrates that crude oil price declines periodically. Comparing with benchmark ARIMA and EEMD-ICA-ARIMA, VMD-ICA-ARIMA can forecast the crude oil price more accurately.

  19. Forecasting electricity spot market prices with a k-factor GIGARCH process

    International Nuclear Information System (INIS)

    Diongue, Abdou Ka; Guegan, Dominique; Vignal, Bertrand

    2009-01-01

    In this article, we investigate conditional mean and conditional variance forecasts using a dynamic model following a k-factor GIGARCH process. Particularly, we provide the analytical expression of the conditional variance of the prediction error. We apply this method to the German electricity price market for the period August 15, 2000-December 31, 2002 and we test spot prices forecasts until one-month ahead forecast. The forecasting performance of the model is compared with a SARIMA-GARCH benchmark model using the year 2003 as the out-of-sample. The proposed model outperforms clearly the benchmark model. We conclude that the k-factor GIGARCH process is a suitable tool to forecast spot prices, using the classical RMSE criteria. (author)

  20. U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change

    OpenAIRE

    Isengildina-Massa, Olga; MacDonald, Stephen

    2009-01-01

    The purpose of this study was to analyze structural changes that took place in the cotton industry in recent years and develop a statistical model that reflects the current drivers of U.S. cotton prices. Legislative changes authorized the U.S. Department of Agriculture to resume publishing cotton price forecasts for the first time in 79 years. In addition, systematic problems have become apparent in the forecasting models used by USDA and elsewhere, highlighting the need for an updated review...

  1. Day-ahead price forecasting in restructured power systems using artificial neural networks

    International Nuclear Information System (INIS)

    Vahidinasab, V.; Jadid, S.; Kazemi, A.

    2008-01-01

    Over the past 15 years most electricity supply companies around the world have been restructured from monopoly utilities to deregulated competitive electricity markets. Market participants in the restructured electricity markets find short-term electricity price forecasting (STPF) crucial in formulating their risk management strategies. They need to know future electricity prices as their profitability depends on them. This research project classifies and compares different techniques of electricity price forecasting in the literature and selects artificial neural networks (ANN) as a suitable method for price forecasting. To perform this task, market knowledge should be used to optimize the selection of input data for an electricity price forecasting tool. Then sensitivity analysis is used in this research to aid in the selection of the optimum inputs of the ANN and fuzzy c-mean (FCM) algorithm is used for daily load pattern clustering. Finally, ANN with a modified Levenberg-Marquardt (LM) learning algorithm are implemented for forecasting prices in Pennsylvania-New Jersey-Maryland (PJM) market. The forecasting results were compared with the previous works and showed that the results are reasonable and accurate. (author)

  2. Olympic games in Sochi, comparison and forecast

    OpenAIRE

    Kudrina, Anastasia

    2015-01-01

    Title: Olympic Games in Sochi, comparison and prediction Goals: Based on a research of the Olympic Games in Sochi to create a forecast of future economic effects on the territorium Method: The prediction is based on the analogic situation in other countries that held the Olympic Games in the past. Induction method of representing the thought process, during which the individual facts formulate more general assertions and theories; deduction method, which are derived from the general conclusio...

  3. Day-ahead deregulated electricity market price forecasting using neural network input featured by DCT

    International Nuclear Information System (INIS)

    Anbazhagan, S.; Kumarappan, N.

    2014-01-01

    Highlights: • We presented DCT input featured FFNN model for forecasting in Spain market. • The key factors impacting electricity price forecasting are historical prices. • Past 42 days were trained and the next 7 days were forecasted. • The proposed approach has a simple and better NN structure. • The DCT-FFNN mode is effective and less computation time than the recent models. - Abstract: In a deregulated market, a number of factors determined the outcome of electricity price and displays a perplexed and maverick fluctuation. Both power producers and consumers needs single compact and robust price forecasting tool in order to maximize their profits and utilities. In order to achieve the helter–skelter kind of electricity price, one dimensional discrete cosine transforms (DCT) input featured feed-forward neural network (FFNN) is modeled (DCT-FFNN). The proposed FFNN is a single compact and robust architecture (without hybridizing the various hard and soft computing models). It has been predicted that the DCT-FFNN model is close to the state of the art can be achieved with less computation time. The proposed DCT-FFNN approach is compared with 17 other recent approaches to estimate the market clearing prices of mainland Spain. Finally, the accuracy of the price forecasting is also applied to the electricity market of New York in year 2010 that shows the effectiveness of the proposed DCT-FFNN approach

  4. Controlling Electricity Consumption by Forecasting its Response to Varying Prices

    DEFF Research Database (Denmark)

    Corradi, Olivier; Ochsenfeld, Henning Peter; Madsen, Henrik

    2013-01-01

    electricity consumption using a one-way price signal. Estimation of the price-response is based on data measurable at grid level, removing the need to install sensors and communication devices between each individual consumer and the price-generating entity. An application for price-responsive heating systems......In a real-time electricity pricing context where consumers are sensitive to varying prices, having the ability to anticipate their response to a price change is valuable. This paper proposes models for the dynamics of such price-response, and shows how these dynamics can be used to control...... is studied based on real data, before conducting a control by price experiment using a mixture of real and synthetic data. With the control objective of following a constant consumption reference, peak heating consumption is reduced by nearly 5%, and 11% of the mean daily heating consumption is shifted....

  5. Preliminary analysis on hybrid Box-Jenkins - GARCH modeling in forecasting gold price

    Science.gov (United States)

    Yaziz, Siti Roslindar; Azizan, Noor Azlinna; Ahmad, Maizah Hura; Zakaria, Roslinazairimah; Agrawal, Manju; Boland, John

    2015-02-01

    Gold has been regarded as a valuable precious metal and the most popular commodity as a healthy return investment. Hence, the analysis and prediction of gold price become very significant to investors. This study is a preliminary analysis on gold price and its volatility that focuses on the performance of hybrid Box-Jenkins models together with GARCH in analyzing and forecasting gold price. The Box-Cox formula is used as the data transformation method due to its potential best practice in normalizing data, stabilizing variance and reduces heteroscedasticity using 41-year daily gold price data series starting 2nd January 1973. Our study indicates that the proposed hybrid model ARIMA-GARCH with t-innovation can be a new potential approach in forecasting gold price. This finding proves the strength of GARCH in handling volatility in the gold price as well as overcomes the non-linear limitation in the Box-Jenkins modeling.

  6. Testing the rationality of DOE's energy price forecasts under asymmetric loss preferences

    International Nuclear Information System (INIS)

    Mamatzakis, E.; Koutsomanoli-Filippaki, A.

    2014-01-01

    This paper examines the rationality of the price forecasts for energy commodities of the United States Department of Energy's (DOE), departing from the common assumption in the literature that DOE's forecasts are based on a symmetric underlying loss function with respect to positive vs. negative forecast errors. Instead, we opt for the methodology of Elliott et al. (2005) that allows testing the joint hypothesis of an asymmetric loss function and rationality and reveals the underlying preferences of the forecaster. Results indicate the existence of asymmetries in the shape of the loss function for most energy categories with preferences leaning towards optimism. Moreover, we also examine whether there is a structural break in those preferences over the examined period, 1997–2012. - Highlights: • Examine the rationality of DOE energy forecasts. • Departing from a symmetric underlying loss function. • Asymmetries exist in most energy prices. • Preferences lean towards optimism. • Examine structural breaks in those preferences

  7. Formation and forecast of the daily price of the electric power in the chain Nare-Guatape-San Carlos

    International Nuclear Information System (INIS)

    Romero, Alejandro; Carvajal, Luis

    2003-01-01

    This work shows three different methodologies for the understanding and forecast of the electric energy prices in the chain Nare - Guatape - San Carlos: lineal multivariate model, autoregressive deterministic model and Fourier series decomposition. The electric energy price depends basically of the reservoir level and river flow, not only its own but the reservoir down and up, waters. About prices forecast, they can be modeled with an autoregressive process. Prices forecast follows the tendency and captures with acceptable precision the maximum prices due especially to the low hydrology and price variability for daily and weekly regulation reservoirs

  8. Price formation in electricity forward markets and the relevance of systematic forecast errors

    International Nuclear Information System (INIS)

    Redl, Christian; Haas, Reinhard; Huber, Claus; Boehm, Bernhard

    2009-01-01

    Since the liberalisation of the European electricity sector, forward and futures contracts have gained significant interest of market participants due to risk management reasons. For pricing of these contracts an important fact concerns the non-storability of electricity. In this case, according to economic theory, forward prices are related to the expected spot prices which are built on fundamental market expectations. In the following article the crucial impact parameters of forward electricity prices and the relationship between forward and future spot prices will be assessed by an empirical analysis of electricity prices at the European Energy Exchange and the Nord Pool Power Exchange. In fact, price formation in the considered markets is influenced by historic spot market prices yielding a biased forecasting power of long-term contracts. Although market and risk assessment measures of market participants and supply and demand shocks can partly explain the futures-spot bias inefficiencies in the analysed forward markets cannot be ruled out. (author)

  9. Price Forecasting of Electricity Markets in the Presence of a High Penetration of Wind Power Generators

    Directory of Open Access Journals (Sweden)

    Saber Talari

    2017-11-01

    Full Text Available Price forecasting plays a vital role in the day-ahead markets. Once sellers and buyers access an accurate price forecasting, managing the economic risk can be conducted appropriately through offering or bidding suitable prices. In networks with high wind power penetration, the electricity price is influenced by wind energy; therefore, price forecasting can be more complicated. This paper proposes a novel hybrid approach for price forecasting of day-ahead markets, with high penetration of wind generators based on Wavelet transform, bivariate Auto-Regressive Integrated Moving Average (ARIMA method and Radial Basis Function Neural Network (RBFN. To this end, a weighted time series for wind dominated power systems is calculated and added to a bivariate ARIMA model along with the price time series. Moreover, RBFN is applied as a tool to correct the estimation error, and particle swarm optimization (PSO is used to optimize the structure and adapt the RBFN to the particular training set. This method is evaluated on the Spanish electricity market, which shows the efficiency of this approach. This method has less error compared with other methods especially when it considers the effects of large-scale wind generators.

  10. Simultaneous day-ahead forecasting of electricity price and load in smart grids

    International Nuclear Information System (INIS)

    Shayeghi, H.; Ghasemi, A.; Moradzadeh, M.; Nooshyar, M.

    2015-01-01

    Highlights: • This paper presents a novel MIMO-based support vector machine for forecasting. • Considered uncertainties for better simulation for filtering in input data. • Used LSSVM technique for learning. • Proposed a new modification for standard artificial bee colony algorithm to optimize LSSVM engine. - Abstract: In smart grids, customers are promoted to change their energy consumption patterns by electricity prices. In fact, in this environment, the electricity price and load consumption are highly corrected such that the market participants will have complex model in their decisions to maximize their profit. Although the available forecasting mythologies perform well in electricity market by way of little or no load and price interdependencies, but cannot capture load and price dynamics if they exist. To overcome this shortage, a Multi-Input Multi-Output (MIMO) model is presented which can consider the correlation between electricity price and load. The proposed model consists of three components known as a Wavelet Packet Transform (WPT) to make valuable subsets, Generalized Mutual Information (GMI) to select best input candidate and Least Squares Support Vector Machine (LSSVM) based on MIMO model, called LSSVM-MIMO, to make simultaneous load and price forecasts. Moreover, the LSSVM-MIMO parameters are optimized by a novel Quasi-Oppositional Artificial Bee Colony (QOABC) algorithm. Some forecasting indices based on error factor are considered to evaluate the forecasting accuracy. Simulations carried out on New York Independent System Operator, New South Wales (NSW) and PJM electricity markets data, and showing that the proposed hybrid algorithm has good potential for simultaneous forecasting of electricity price and load

  11. Robust estimation and forecasting of the long-term seasonal component of electricity spot prices

    International Nuclear Information System (INIS)

    Nowotarski, Jakub; Tomczyk, Jakub; Weron, Rafał

    2013-01-01

    We present the results of an extensive study on estimation and forecasting of the long-term seasonal component (LTSC) of electricity spot prices. We consider a battery of over 300 models, including monthly dummies and models based on Fourier or wavelet decomposition combined with linear or exponential decay. We find that the considered wavelet-based models are significantly better in terms of forecasting spot prices up to a year ahead than the commonly used monthly dummies and sine-based models. This result questions the validity and usefulness of stochastic models of spot electricity prices built on the latter two types of LTSC models. - Highlights: • First comprehensive study on the forecasting of the long-term seasonal components • Over 300 models examined, including commonly used and new approaches • Wavelet-based models outperform sine-based and monthly dummy models. • Validity of stochastic models built on sines or monthly dummies is questionable

  12. Day-ahead price forecasting of electricity markets by a new feature selection algorithm and cascaded neural network technique

    International Nuclear Information System (INIS)

    Amjady, Nima; Keynia, Farshid

    2009-01-01

    With the introduction of restructuring into the electric power industry, the price of electricity has become the focus of all activities in the power market. Electricity price forecast is key information for electricity market managers and participants. However, electricity price is a complex signal due to its non-linear, non-stationary, and time variant behavior. In spite of performed research in this area, more accurate and robust price forecast methods are still required. In this paper, a new forecast strategy is proposed for day-ahead price forecasting of electricity markets. Our forecast strategy is composed of a new two stage feature selection technique and cascaded neural networks. The proposed feature selection technique comprises modified Relief algorithm for the first stage and correlation analysis for the second stage. The modified Relief algorithm selects candidate inputs with maximum relevancy with the target variable. Then among the selected candidates, the correlation analysis eliminates redundant inputs. Selected features by the two stage feature selection technique are used for the forecast engine, which is composed of 24 consecutive forecasters. Each of these 24 forecasters is a neural network allocated to predict the price of 1 h of the next day. The whole proposed forecast strategy is examined on the Spanish and Australia's National Electricity Markets Management Company (NEMMCO) and compared with some of the most recent price forecast methods.

  13. A Comparative Study Of Stock Price Forecasting Using Nonlinear Models

    Directory of Open Access Journals (Sweden)

    Diteboho Xaba

    2017-03-01

    Full Text Available This study compared the in-sample forecasting accuracy of three forecasting nonlinear models namely: the Smooth Transition Regression (STR model, the Threshold Autoregressive (TAR model and the Markov-switching Autoregressive (MS-AR model. Nonlinearity tests were used to confirm the validity of the assumptions of the study. The study used model selection criteria, SBC to select the optimal lag order and for the selection of appropriate models. The Mean Square Error (MSE, Mean Absolute Error (MAE and Root Mean Square Error (RMSE served as the error measures in evaluating the forecasting ability of the models. The MS-AR models proved to perform well with lower error measures as compared to LSTR and TAR models in most cases.

  14. Modeling and Forecasting Average Temperature for Weather Derivative Pricing

    Directory of Open Access Journals (Sweden)

    Zhiliang Wang

    2015-01-01

    Full Text Available The main purpose of this paper is to present a feasible model for the daily average temperature on the area of Zhengzhou and apply it to weather derivatives pricing. We start by exploring the background of weather derivatives market and then use the 62 years of daily historical data to apply the mean-reverting Ornstein-Uhlenbeck process to describe the evolution of the temperature. Finally, Monte Carlo simulations are used to price heating degree day (HDD call option for this city, and the slow convergence of the price of the HDD call can be found through taking 100,000 simulations. The methods of the research will provide a frame work for modeling temperature and pricing weather derivatives in other similar places in China.

  15. Forecasting inflation based on the consumer price index, taking into account the impact of seasonal factors

    Directory of Open Access Journals (Sweden)

    A. K. Sapova

    2017-01-01

    Full Text Available The consumer price index is a key indicator of the inflation level in Russia. It is important for the Central Bank and Government in decision-making process. There is a strong need for high-quality analysis and accurate forecast of this index. Modelling and forecasting of consumer price index as a key indicator of inflation are relevant issues in current macroeconomic conditions. The article is dedicated to development of quality short-term forecast of consumer inflation level, with the impact of seasonal factor. Two classes of models (vector autoregression and time series models are considered. It was shown that vector autoregression model of the dependency between consumer price index and nominal effective exchange rate is worse for the proposes of inflation forecast then non-linear model with structural components and conventional heteroscedasticity. The practical significance of this work is that the developed approach to the forecasting of the consumer price index adjusted of seasonal factor can be very helpful for the purpose of proper assessment and regulation of inflation.

  16. A regime-switching stochastic volatility model for forecasting electricity prices

    DEFF Research Database (Denmark)

    Exterkate, Peter; Knapik, Oskar

    In a recent review paper, Weron (2014) pinpoints several crucial challenges outstanding in the area of electricity price forecasting. This research attempts to address all of them by i) showing the importance of considering fundamental price drivers in modeling, ii) developing new techniques for ...... on explanatory variables. Bayesian inference is explored in order to obtain predictive densities. The main focus of the paper is on shorttime density forecasting in Nord Pool intraday market. We show that the proposed model outperforms several benchmark models at this task....

  17. Forecasting Natural Gas Prices Using Wavelets, Time Series, and Artificial Neural Networks.

    Directory of Open Access Journals (Sweden)

    Junghwan Jin

    Full Text Available Following the unconventional gas revolution, the forecasting of natural gas prices has become increasingly important because the association of these prices with those of crude oil has weakened. With this as motivation, we propose some modified hybrid models in which various combinations of the wavelet approximation, detail components, autoregressive integrated moving average, generalized autoregressive conditional heteroskedasticity, and artificial neural network models are employed to predict natural gas prices. We also emphasize the boundary problem in wavelet decomposition, and compare results that consider the boundary problem case with those that do not. The empirical results show that our suggested approach can handle the boundary problem, such that it facilitates the extraction of the appropriate forecasting results. The performance of the wavelet-hybrid approach was superior in all cases, whereas the application of detail components in the forecasting was only able to yield a small improvement in forecasting performance. Therefore, forecasting with only an approximation component would be acceptable, in consideration of forecasting efficiency.

  18. An enhanced radial basis function network for short-term electricity price forecasting

    International Nuclear Information System (INIS)

    Lin, Whei-Min; Gow, Hong-Jey; Tsai, Ming-Tang

    2010-01-01

    This paper proposed a price forecasting system for electric market participants to reduce the risk of price volatility. Combining the Radial Basis Function Network (RBFN) and Orthogonal Experimental Design (OED), an Enhanced Radial Basis Function Network (ERBFN) has been proposed for the solving process. The Locational Marginal Price (LMP), system load, transmission flow and temperature of the PJM system were collected and the data clusters were embedded in the Excel Database according to the year, season, workday and weekend. With the OED applied to learning rates in the ERBFN, the forecasting error can be reduced during the training process to improve both accuracy and reliability. This would mean that even the ''spikes'' could be tracked closely. The Back-propagation Neural Network (BPN), Probability Neural Network (PNN), other algorithms, and the proposed ERBFN were all developed and compared to check the performance. Simulation results demonstrated the effectiveness of the proposed ERBFN to provide quality information in a price volatile environment. (author)

  19. Natural gas, NGL's and crude: supply, demand and price forecasts

    International Nuclear Information System (INIS)

    Stauft, T.L.

    2003-01-01

    This paper presents an overview of the major issues to watch in the crude oil, natural gas, and natural gas liquids (NGL) markets in North America. The presentation began with background information concerning Purvin and Gertz, an employee-owned consulting firm whose employees are chemical engineers, holders of a Master of Business Administration (MBA), or economists. They specialize in providing strategic, commercial, and technical advice to the international energy industry. A closer look at each individual market was provided, looking at demand, supply, price drivers and others. The author concluded that world oil prices continue to be influenced by a war premium. Oil prices support natural gas, as well as the possibility of a supply issue. The gas processing margins have remained strong. The unknown quantities are the weather and economic recovery. figs

  20. Stock prices forecasting based on wavelet neural networks with PSO

    OpenAIRE

    Wang Kai-Cheng; Yang Chi-I; Chang Kuei-Fang

    2017-01-01

    This research examines the forecasting performance of wavelet neural network (WNN) model using published stock data obtained from Financial Times Stock Exchange (FTSE) Taiwan Stock Exchange (TWSE) 50 index, also known as Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), hereinafter referred to as Taiwan 50. Our WNN model uses particle swarm optimization (PSO) to choose the appropriate initial network values for different companies. The findings come with two advantages. First...

  1. A mathematical model for stock price forecasting | Ogwuche | West ...

    African Journals Online (AJOL)

    ) and the covariance (the volatility) of the change were computed leading to the formulation of the system of linear stochastic differential equations. To fit data to the model, changes in the prices of the stocks were studied for an average of 30 ...

  2. Forecasting ability of the investor sentiment endurance index: The case of oil service stock returns and crude oil prices

    International Nuclear Information System (INIS)

    He, Ling T.; Casey, K.M.

    2015-01-01

    Using a binomial probability distribution model this paper creates an endurance index of oil service investor sentiment. The index reflects the probability of the high or low stock price being the close price for the PHLX Oil Service Sector Index. Results of this study reveal the substantial forecasting ability of the sentiment endurance index. Monthly and quarterly rolling forecasts of returns of oil service stocks have an overall accuracy as high as 52% to 57%. In addition, the index shows decent forecasting ability on changes in crude oil prices, especially, WTI prices. The accuracy of 6-quarter rolling forecasts is 55%. The sentiment endurance index, along with the procedure of true forecasting and accuracy ratio, applied in this study provides investors and analysts of oil service sector stocks and crude oil prices as well as energy policy-makers with effective analytical tools

  3. Electric power demand forecasting using interval time series. A comparison between VAR and iMLP

    International Nuclear Information System (INIS)

    Garcia-Ascanio, Carolina; Mate, Carlos

    2010-01-01

    Electric power demand forecasts play an essential role in the electric industry, as they provide the basis for making decisions in power system planning and operation. A great variety of mathematical methods have been used for demand forecasting. The development and improvement of appropriate mathematical tools will lead to more accurate demand forecasting techniques. In order to forecast the monthly electric power demand per hour in Spain for 2 years, this paper presents a comparison between a new forecasting approach considering vector autoregressive (VAR) forecasting models applied to interval time series (ITS) and the iMLP, the multi-layer perceptron model adapted to interval data. In the proposed comparison, for the VAR approach two models are fitted per every hour, one composed of the centre (mid-point) and radius (half-range), and another one of the lower and upper bounds according to the interval representation assumed by the ITS in the learning set. In the case of the iMLP, only the model composed of the centre and radius is fitted. The other interval representation composed of the lower and upper bounds is obtained from the linear combination of the two. This novel approach, obtaining two bivariate models each hour, makes possible to establish, for different periods in the day, which interval representation is more accurate. Furthermore, the comparison between two different techniques adapted to interval time series allows us to determine the efficiency of these models in forecasting electric power demand. It is important to note that the iMLP technique has been selected for the comparison, as it has shown its accuracy in forecasting daily electricity price intervals. This work shows the ITS forecasting methods as a potential tool that will lead to a reduction in risk when making power system planning and operational decisions. (author)

  4. Multivariate Time Series Forecasting of Crude Palm Oil Price Using Machine Learning Techniques

    Science.gov (United States)

    Kanchymalay, Kasturi; Salim, N.; Sukprasert, Anupong; Krishnan, Ramesh; Raba'ah Hashim, Ummi

    2017-08-01

    The aim of this paper was to study the correlation between crude palm oil (CPO) price, selected vegetable oil prices (such as soybean oil, coconut oil, and olive oil, rapeseed oil and sunflower oil), crude oil and the monthly exchange rate. Comparative analysis was then performed on CPO price forecasting results using the machine learning techniques. Monthly CPO prices, selected vegetable oil prices, crude oil prices and monthly exchange rate data from January 1987 to February 2017 were utilized. Preliminary analysis showed a positive and high correlation between the CPO price and soy bean oil price and also between CPO price and crude oil price. Experiments were conducted using multi-layer perception, support vector regression and Holt Winter exponential smoothing techniques. The results were assessed by using criteria of root mean square error (RMSE), means absolute error (MAE), means absolute percentage error (MAPE) and Direction of accuracy (DA). Among these three techniques, support vector regression(SVR) with Sequential minimal optimization (SMO) algorithm showed relatively better results compared to multi-layer perceptron and Holt Winters exponential smoothing method.

  5. Statistical model for forecasting uranium prices to estimate the nuclear fuel cycle cost

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Sung Ki; Ko, Won Il; Nam, Hyoon [Nuclear Fuel Cycle Analysis, Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Kim, Chul Min; Chung, Yang Hon; Bang, Sung Sig [Korea Advanced Institute of Science and Technology, Daejeon (Korea, Republic of)

    2017-08-15

    This paper presents a method for forecasting future uranium prices that is used as input data to calculate the uranium cost, which is a rational key cost driver of the nuclear fuel cycle cost. In other words, the statistical autoregressive integrated moving average (ARIMA) model and existing engineering cost estimation method, the so-called escalation rate model, were subjected to a comparative analysis. When the uranium price was forecasted in 2015, the margin of error of the ARIMA model forecasting was calculated and found to be 5.4%, whereas the escalation rate model was found to have a margin of error of 7.32%. Thus, it was verified that the ARIMA model is more suitable than the escalation rate model at decreasing uncertainty in nuclear fuel cycle cost calculation.

  6. Statistical model for forecasting uranium prices to estimate the nuclear fuel cycle cost

    International Nuclear Information System (INIS)

    Kim, Sung Ki; Ko, Won Il; Nam, Hyoon; Kim, Chul Min; Chung, Yang Hon; Bang, Sung Sig

    2017-01-01

    This paper presents a method for forecasting future uranium prices that is used as input data to calculate the uranium cost, which is a rational key cost driver of the nuclear fuel cycle cost. In other words, the statistical autoregressive integrated moving average (ARIMA) model and existing engineering cost estimation method, the so-called escalation rate model, were subjected to a comparative analysis. When the uranium price was forecasted in 2015, the margin of error of the ARIMA model forecasting was calculated and found to be 5.4%, whereas the escalation rate model was found to have a margin of error of 7.32%. Thus, it was verified that the ARIMA model is more suitable than the escalation rate model at decreasing uncertainty in nuclear fuel cycle cost calculation

  7. Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models

    International Nuclear Information System (INIS)

    Nguyen, Hang T.; Nabney, Ian T.

    2010-01-01

    This paper presents some forecasting techniques for energy demand and price prediction, one day ahead. These techniques combine wavelet transform (WT) with fixed and adaptive machine learning/time series models (multi-layer perceptron (MLP), radial basis functions, linear regression, or GARCH). To create an adaptive model, we use an extended Kalman filter or particle filter to update the parameters continuously on the test set. The adaptive GARCH model is a new contribution, broadening the applicability of GARCH methods. We empirically compared two approaches of combining the WT with prediction models: multicomponent forecasts and direct forecasts. These techniques are applied to large sets of real data (both stationary and non-stationary) from the UK energy markets, so as to provide comparative results that are statistically stronger than those previously reported. The results showed that the forecasting accuracy is significantly improved by using the WT and adaptive models. The best models on the electricity demand/gas price forecast are the adaptive MLP/GARCH with the multicomponent forecast; their NMSEs are 0.02314 and 0.15384 respectively. (author)

  8. Do Exchange Rates Really Help Forecasting Commodity Prices?

    DEFF Research Database (Denmark)

    Bork, Lasse; Kaltwasser, Pablo Rovira; Sercu, Piet

    Chen et al. (2010) report that for ‘commodity currencies’, the exchange rate predicts the country’s commodity index but not vice versa. The commodity currency hypothesis is consistent with the Engle and West (2005) exchange rate model if the fundamental is chosen to be the country’s key export...... expectations, one should mostly observe contemporaneous correlations, not one-directional cross-predictability from one variable toward the other. Using three different data sets and various econometric techniques, we do find the contemporaneous correlations as predicted by the financial asset view......-averaged prices in the commodity index data that they use (price averaging induces spurious autocorrelation and predictability) and to features in their test procedures....

  9. Forecasting short-run crude oil price using high- and low-inventory variables

    International Nuclear Information System (INIS)

    Ye, Michael; Zyren, John; Shore, Joanne

    2006-01-01

    Since inventories have a lower bound or a minimum operating level, economic literature suggests a nonlinear relationship between inventory level and commodity prices. This was found to be the case in the short-run crude oil market. In order to explore this inventory-price relationship, two nonlinear inventory variables are defined and derived from the monthly normal level and relative level of OECD crude oil inventories from post 1991 Gulf War to October 2003: one for the low inventory state and another for the high inventory state of the crude oil market. Incorporation of low- and high-inventory variables in a single equation model to forecast short-run WTI crude oil prices enhances the model fit and forecast ability

  10. Day-ahead electricity prices forecasting by a modified CGSA technique and hybrid WT in LSSVM based scheme

    International Nuclear Information System (INIS)

    Shayeghi, H.; Ghasemi, A.

    2013-01-01

    Highlights: • Presenting a hybrid CGSA-LSSVM scheme for price forecasting. • Considering uncertainties for filtering in input data and feature selection to improve efficiency. • Using DWT input featured LSSVM approach to classify next-week prices. • Used three real markets to illustrate performance of the proposed price forecasting model. - Abstract: At the present time, day-ahead electricity market is closely associated with other commodity markets such as fuel market and emission market. Under such an environment, day-ahead electricity price forecasting has become necessary for power producers and consumers in the current deregulated electricity markets. Seeking for more accurate price forecasting techniques, this paper proposes a new combination of a Feature Selection (FS) technique based mutual information (MI) technique and Wavelet Transform (WT) in this study. Moreover, in this paper a new modified version of Gravitational Search Algorithm (GSA) optimization based chaos theory, namely Chaotic Gravitational Search Algorithm (CGSA) is developed to find the optimal parameters of Least Square Support Vector Machine (LSSVM) to predict electricity prices. The performance and price forecast accuracy of the proposed technique is assessed by means of real data from Iran’s, Ontario’s and Spain’s price markets. The simulation results from numerical tables and figures in different cases show that the proposed technique increases electricity price market forecasting accuracy than the other classical and heretical methods in the scientific researches

  11. Demand, supply and fuel prices forecast to the year 2000

    International Nuclear Information System (INIS)

    1984-01-01

    This paper summarizes the Western European energy situation, and deals with specific aspects under the headings: European oil prices fall until 1987; prospects for oil recovery; transport sector holds oil demand up as oil demand loses favour in other sectors; upstream uncertainties; continued slackness of European natural gas market poses threat to oil; problems for European coal industry; dramatic growth in nuclear power; breeder reactors to play minimal role; PWRs will remain dominant. The situation in individual countries - Belgium, the Netherlands, France, Germany, United Kingdom, Italy and Spain - is analysed. (U.K.)

  12. Stock prices forecasting based on wavelet neural networks with PSO

    Directory of Open Access Journals (Sweden)

    Wang Kai-Cheng

    2017-01-01

    Full Text Available This research examines the forecasting performance of wavelet neural network (WNN model using published stock data obtained from Financial Times Stock Exchange (FTSE Taiwan Stock Exchange (TWSE 50 index, also known as Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX, hereinafter referred to as Taiwan 50. Our WNN model uses particle swarm optimization (PSO to choose the appropriate initial network values for different companies. The findings come with two advantages. First, the network initial values are automatically selected instead of being a constant. Second, threshold and training data percentage become constant values, because PSO assists with self-adjustment. We can achieve a success rate over 73% without the necessity to manually adjust parameter or create another math model.

  13. Fuzzy time-series based on Fibonacci sequence for stock price forecasting

    Science.gov (United States)

    Chen, Tai-Liang; Cheng, Ching-Hsue; Jong Teoh, Hia

    2007-07-01

    Time-series models have been utilized to make reasonably accurate predictions in the areas of stock price movements, academic enrollments, weather, etc. For promoting the forecasting performance of fuzzy time-series models, this paper proposes a new model, which incorporates the concept of the Fibonacci sequence, the framework of Song and Chissom's model and the weighted method of Yu's model. This paper employs a 5-year period TSMC (Taiwan Semiconductor Manufacturing Company) stock price data and a 13-year period of TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) stock index data as experimental datasets. By comparing our forecasting performances with Chen's (Forecasting enrollments based on fuzzy time-series. Fuzzy Sets Syst. 81 (1996) 311-319), Yu's (Weighted fuzzy time-series models for TAIEX forecasting. Physica A 349 (2004) 609-624) and Huarng's (The application of neural networks to forecast fuzzy time series. Physica A 336 (2006) 481-491) models, we conclude that the proposed model surpasses in accuracy these conventional fuzzy time-series models.

  14. Forecasting Long-Term Crude Oil Prices Using a Bayesian Model with Informative Priors

    Directory of Open Access Journals (Sweden)

    Chul-Yong Lee

    2017-01-01

    Full Text Available In the long-term, crude oil prices may impact the economic stability and sustainability of many countries, especially those depending on oil imports. This study thus suggests an alternative model for accurately forecasting oil prices while reflecting structural changes in the oil market by using a Bayesian approach. The prior information is derived from the recent and expected structure of the oil market, using a subjective approach, and then updated with available market data. The model includes as independent variables factors affecting oil prices, such as world oil demand and supply, the financial situation, upstream costs, and geopolitical events. To test the model’s forecasting performance, it is compared with other models, including a linear ordinary least squares model and a neural network model. The proposed model outperforms on the forecasting performance test even though the neural network model shows the best results on a goodness-of-fit test. The results show that the crude oil price is estimated to increase to $169.3/Bbl by 2040.

  15. Forecasting Construction Tender Price Index in Ghana using Autoregressive Integrated Moving Average with Exogenous Variables Model

    Directory of Open Access Journals (Sweden)

    Ernest Kissi

    2018-03-01

    Full Text Available Prices of construction resources keep on fluctuating due to unstable economic situations that have been experienced over the years. Clients knowledge of their financial commitments toward their intended project remains the basis for their final decision. The use of construction tender price index provides a realistic estimate at the early stage of the project. Tender price index (TPI is influenced by various economic factors, hence there are several statistical techniques that have been employed in forecasting. Some of these include regression, time series, vector error correction among others. However, in recent times the integrated modelling approach is gaining popularity due to its ability to give powerful predictive accuracy. Thus, in line with this assumption, the aim of this study is to apply autoregressive integrated moving average with exogenous variables (ARIMAX in modelling TPI. The results showed that ARIMAX model has a better predictive ability than the use of the single approach. The study further confirms the earlier position of previous research of the need to use the integrated model technique in forecasting TPI. This model will assist practitioners to forecast the future values of tender price index. Although the study focuses on the Ghanaian economy, the findings can be broadly applicable to other developing countries which share similar economic characteristics.

  16. Forecasting Nord Pool day-ahead prices with an autoregressive model

    International Nuclear Information System (INIS)

    Kristiansen, Tarjei

    2012-01-01

    This paper presents a model to forecast Nord Pool hourly day-ahead prices. The model is based on but reduced in terms of estimation parameters (from 24 sets to 1) and modified to include Nordic demand and Danish wind power as exogenous variables. We model prices across all hours in the analysis period rather than across each single hour of 24 hours. By applying three model variants on Nord Pool data, we achieve a weekly mean absolute percentage error (WMAE) of around 6–7% and an hourly mean absolute percentage error (MAPE) ranging from 8% to 11%. Out of sample results yields a WMAE and an hourly MAPE of around 5%. The models enable analysts and traders to forecast hourly day-ahead prices accurately. Moreover, the models are relatively straightforward and user-friendly to implement. They can be set up in any trading organization. - Highlights: ► Forecasting Nord Pool day-ahead prices with an autoregressive model. ► The model is based on but with the set of parameters reduced from 24 to 1. ► The model includes Nordic demand and Danish wind power as exogenous variables. ► Hourly mean absolute percentage error ranges from 8% to 11%. ► Out of sample results yields a WMAE and an hourly MAPE of around 5%.

  17. Electricity spot price forecasting in free power market

    International Nuclear Information System (INIS)

    Lilleberg, J.; Laitinen, E.K.

    1998-01-01

    Deregulation has brought many changes to the electricity market. Freedom of choice has been granted to both the consumers and the utilities. Consumers may choose the seller of their energy. Utilities have a wider array of sources to acquire their electricity from. Also the types of sales contracts used are changing to fill the needs of this new situation. The consumers' right to choose has introduced a new risk uncertainty of volume, which was not true during the times of monopoly. As sold volume is unsure and the energy is not sold on same terms as it is bought, a price risk has to be dealt with also. The electric utility has to realize this, select a risk level that suits its business strategy and optimize its actions according to the selected risk level. The number of participants will grow as the electricity market integrates into a common market for Scandinavia and even Europe. Big customers are also taking a more active role in the market, further increasing the number of participants. This makes old bilateral arrangements outdated. New tools are needed to control the new business environment. The goal of this project has been to develop a theoretical model to predict the price in the Finnish electricity exchange, El-Ex Oy. An extensive literature review was conducted in order to (1) examine the solutions in deregulation of electricity markets in other countries, esp. in Norway and UK, (2) find similarities and differences in electricity exchange and exchanges generally and (3) find major sources of problems and inefficiency in the market

  18. Electricity spot price forecasting in free power market

    Energy Technology Data Exchange (ETDEWEB)

    Lilleberg, J; Laitinen, E K [Vaasa Univ. (Finland)

    1998-08-01

    Deregulation has brought many changes to the electricity market. Freedom of choice has been granted to both the consumers and the utilities. Consumers may choose the seller of their energy. Utilities have a wider array of sources to acquire their electricity from. Also the types of sales contracts used are changing to fill the needs of this new situation. The consumers` right to choose has introduced a new risk uncertainty of volume, which was not true during the times of monopoly. As sold volume is unsure and the energy is not sold on same terms as it is bought, a price risk has to be dealt with also. The electric utility has to realize this, select a risk level that suits its business strategy and optimize its actions according to the selected risk level. The number of participants will grow as the electricity market integrates into a common market for Scandinavia and even Europe. Big customers are also taking a more active role in the market, further increasing the number of participants. This makes old bilateral arrangements outdated. New tools are needed to control the new business environment. The goal of this project has been to develop a theoretical model to predict the price in the Finnish electricity exchange, El-Ex Oy. An extensive literature review was conducted in order to (1) examine the solutions in deregulation of electricity markets in other countries, esp. in Norway and UK, (2) find similarities and differences in electricity exchange and exchanges generally and (3) find major sources of problems and inefficiency in the market

  19. An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks

    Directory of Open Access Journals (Sweden)

    Ping-Huan Kuo

    2018-04-01

    Full Text Available Electricity price is a key influencer in the electricity market. Electricity market trades by each participant are based on electricity price. The electricity price adjusted with the change in supply and demand relationship can reflect the real value of electricity in the transaction process. However, for the power generating party, bidding strategy determines the level of profit, and the accurate prediction of electricity price could make it possible to determine a more accurate bidding price. This cannot only reduce transaction risk, but also seize opportunities in the electricity market. In order to effectively estimate electricity price, this paper proposes an electricity price forecasting system based on the combination of 2 deep neural networks, the Convolutional Neural Network (CNN and the Long Short Term Memory (LSTM. In order to compare the overall performance of each algorithm, the Mean Absolute Error (MAE and Root-Mean-Square error (RMSE evaluating measures were applied in the experiments of this paper. Experiment results show that compared with other traditional machine learning methods, the prediction performance of the estimating model proposed in this paper is proven to be the best. By combining the CNN and LSTM models, the feasibility and practicality of electricity price prediction is also confirmed in this paper.

  20. Wind Power Forecasting Error Distributions: An International Comparison

    DEFF Research Database (Denmark)

    Hodge, Bri-Mathias; Lew, Debra; Milligan, Michael

    2012-01-01

    Wind power forecasting is essential for greater penetration of wind power into electricity systems. Because no wind forecasting system is perfect, a thorough understanding of the errors that may occur is a critical factor for system operation functions, such as the setting of operating reserve...... levels. This paper provides an international comparison of the distribution of wind power forecasting errors from operational systems, based on real forecast data. The paper concludes with an assessment of similarities and differences between the errors observed in different locations....

  1. Market efficiency, cross hedging and price forecasts: California's natural-gas markets

    International Nuclear Information System (INIS)

    Woo, C.K.; Olson, A.; Horowitz, I.

    2006-01-01

    An extensive North American pipeline grid that physically integrates individual natural-gas markets, in conjunction with economic ties binding the California markets to those at Henry Hub, Louisiana and the New York mercantile exchange via an array of financial instruments, suggests that the spot prices at Henry Hub will impact those in California. We verify the suggestion via a partial-adjustment regression model, thus affirming that California traders can exploit the cross-hedging opportunities made available to them via market integration with Henry Hub, and that they can accurately forecast the price they will have to pay to meet future demand based solely on the price of futures at Henry Hub and the price of a California natural-gas basis swaps contract. (author)

  2. Black-Scholes finite difference modeling in forecasting of call warrant prices in Bursa Malaysia

    Science.gov (United States)

    Mansor, Nur Jariah; Jaffar, Maheran Mohd

    2014-07-01

    Call warrant is a type of structured warrant in Bursa Malaysia. It gives the holder the right to buy the underlying share at a specified price within a limited period of time. The issuer of the structured warrants usually uses European style to exercise the call warrant on the maturity date. Warrant is very similar to an option. Usually, practitioners of the financial field use Black-Scholes model to value the option. The Black-Scholes equation is hard to solve analytically. Therefore the finite difference approach is applied to approximate the value of the call warrant prices. The central in time and central in space scheme is produced to approximate the value of the call warrant prices. It allows the warrant holder to forecast the value of the call warrant prices before the expiry date.

  3. ℓ(p)-Norm multikernel learning approach for stock market price forecasting.

    Science.gov (United States)

    Shao, Xigao; Wu, Kun; Liao, Bifeng

    2012-01-01

    Linear multiple kernel learning model has been used for predicting financial time series. However, ℓ(1)-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adopt ℓ(p)-norm multiple kernel support vector regression (1 ≤ p stock price prediction model. The optimization problem is decomposed into smaller subproblems, and the interleaved optimization strategy is employed to solve the regression model. The model is evaluated on forecasting the daily stock closing prices of Shanghai Stock Index in China. Experimental results show that our proposed model performs better than ℓ(1)-norm multiple support vector regression model.

  4. A novel improved fuzzy support vector machine based stock price trend forecast model

    OpenAIRE

    Wang, Shuheng; Li, Guohao; Bao, Yifan

    2018-01-01

    Application of fuzzy support vector machine in stock price forecast. Support vector machine is a new type of machine learning method proposed in 1990s. It can deal with classification and regression problems very successfully. Due to the excellent learning performance of support vector machine, the technology has become a hot research topic in the field of machine learning, and it has been successfully applied in many fields. However, as a new technology, there are many limitations to support...

  5. The Optimal Confidence Intervals for Agricultural Products’ Price Forecasts Based on Hierarchical Historical Errors

    Directory of Open Access Journals (Sweden)

    Yi Wang

    2016-12-01

    Full Text Available With the levels of confidence and system complexity, interval forecasts and entropy analysis can deliver more information than point forecasts. In this paper, we take receivers’ demands as our starting point, use the trade-off model between accuracy and informativeness as the criterion to construct the optimal confidence interval, derive the theoretical formula of the optimal confidence interval and propose a practical and efficient algorithm based on entropy theory and complexity theory. In order to improve the estimation precision of the error distribution, the point prediction errors are STRATIFIED according to prices and the complexity of the system; the corresponding prediction error samples are obtained by the prices stratification; and the error distributions are estimated by the kernel function method and the stability of the system. In a stable and orderly environment for price forecasting, we obtain point prediction error samples by the weighted local region and RBF (Radial basis function neural network methods, forecast the intervals of the soybean meal and non-GMO (Genetically Modified Organism soybean continuous futures closing prices and implement unconditional coverage, independence and conditional coverage tests for the simulation results. The empirical results are compared from various interval evaluation indicators, different levels of noise, several target confidence levels and different point prediction methods. The analysis shows that the optimal interval construction method is better than the equal probability method and the shortest interval method and has good anti-noise ability with the reduction of system entropy; the hierarchical estimation error method can obtain higher accuracy and better interval estimation than the non-hierarchical method in a stable system.

  6. Bayesian Forecasting of Options Prices: A Natural Framework for Pooling Historical and Implied Volatiltiy Information

    OpenAIRE

    Darsinos, T.; Satchell, S.E.

    2001-01-01

    Bayesian statistical methods are naturally oriented towards pooling in a rigorous way information from separate sources. It has been suggested that both historical and implied volatilities convey information about future volatility. However, typically in the literature implied and return volatility series are fed separately into models to provide rival forecasts of volatility or options prices. We develop a formal Bayesian framework where we can merge the backward looking information as r...

  7. Oil Price Forecasting Using Crack Spread Futures and Oil Exchange Traded Funds

    Directory of Open Access Journals (Sweden)

    Hankyeung Choi

    2015-04-01

    Full Text Available Given the emerging consensus from previous studies that crude oil and refined product (as well as crack spread prices are cointegrated, this study examines the link between the crude oil spot and crack spread derivatives markets. Specifically, the usefulness of the two crack spread derivatives products (namely, crack spread futures and the ETF crack spread for modeling and forecasting daily OPEC crude oil spot prices is evaluated. Based on the results of a structural break test, the sample is divided into pre-crisis, crisis, and post-crisis periods. We find a unidirectional relationship from the two crack spread derivatives markets to the crude oil spot market during the post-crisis period. In terms of forecasting performance, the forecasting models based on crack spread futures and the ETF crack spread outperform the Random Walk Model (RWM, both in-sample and out-of-sample. In addition, on average, the results suggest that information from the ETF crack spread market contributes more to the forecasting models than information from the crack spread futures market.

  8. Forecasting energy market indices with recurrent neural networks: Case study of crude oil price fluctuations

    International Nuclear Information System (INIS)

    Wang, Jie; Wang, Jun

    2016-01-01

    In an attempt to improve the forecasting accuracy of crude oil price fluctuations, a new neural network architecture is established in this work which combines Multilayer perception and ERNN (Elman recurrent neural networks) with stochastic time effective function. ERNN is a time-varying predictive control system and is developed with the ability to keep memory of recent events in order to predict future output. The stochastic time effective function represents that the recent information has a stronger effect for the investors than the old information. With the established model the empirical research has a good performance in testing the predictive effects on four different time series indices. Compared to other models, the present model is possible to evaluate data from 1990s to today with extreme accuracy and speedy. The applied CID (complexity invariant distance) analysis and multiscale CID analysis, are provided as the new useful measures to evaluate a better predicting ability of the proposed model than other traditional models. - Highlights: • A new forecasting model is developed by a random Elman recurrent neural network. • The forecasting accuracy of crude oil price fluctuations is improved by the model. • The forecasting results of the proposed model are more accurate than compared models. • Two new distance analysis methods are applied to confirm the predicting results.

  9. Synthetic river flow time series generator for dispatch and spot price forecast

    International Nuclear Information System (INIS)

    Flores, R.A.

    2007-01-01

    Decision-making in electricity markets is complicated by uncertainties in demand growth, power supplies and fuel prices. In Peru, where the electrical power system is highly dependent on water resources at dams and river flows, hydrological uncertainties play a primary role in planning, price and dispatch forecast. This paper proposed a signal processing method for generating new synthetic river flow time series as a support for planning and spot market price forecasting. River flow time series are natural phenomena representing a continuous-time domain process. As an alternative synthetic representation of the original river flow time series, this proposed signal processing method preserves correlations, basic statistics and seasonality. It takes into account deterministic, periodic and non periodic components such as those due to the El Nino Southern Oscillation phenomenon. The new synthetic time series has many correlations with the original river flow time series, rendering it suitable for possible replacement of the classical method of sorting historical river flow time series. As a dispatch and planning approach to spot pricing, the proposed method offers higher accuracy modeling by decomposing the signal into deterministic, periodic, non periodic and stochastic sub signals. 4 refs., 4 tabs., 13 figs

  10. THE ANALYSIS OF THE COMMODITY PRICE FORECASTING SUCCESS CONSIDERING DIFFERENT LENGTHS OF THE INITIAL CONDITION DRIFT

    Directory of Open Access Journals (Sweden)

    Marcela Lascsáková

    2015-09-01

    Full Text Available In the paper the numerical model based on the exponential approximation of commodity stock exchanges was derived. The price prognoses of aluminium on the London Metal Exchange were determined as numerical solution of the Cauchy initial problem for the 1st order ordinary differential equation. To make the numerical model more accurate the idea of the modification of the initial condition value by the stock exchange was realized. By having analyzed the forecasting success of the chosen initial condition drift types, the initial condition drift providing the most accurate prognoses for the commodity price movements was determined. The suggested modification of the original model made the commodity price prognoses more accurate.

  11. International price comparisons for pharmaceuticals. Measurement and policy issues.

    Science.gov (United States)

    Danzon, P M; Kim, J D

    1998-01-01

    Cross-national price comparisons for pharmaceuticals are commonly used for two purposes. Comparisons based on a sample of products are used to draw conclusions about differences in average price levels. Cross-national comparisons applied to individual products are also used by governments to set domestic prices. This paper examines the major methodological issues raised by international price comparisons, focusing on measurement of differences in average price levels and the validity of policy conclusions drawn from such price comparison studies. It argues that valid measures of average price levels can only be obtained from comparisons based on a comprehensive or representative sample of products, appropriately weighted, following standard index number methods. Comparisons of individual product prices should take into account the manufacturer's entire product portfolio over time rather than focus narrowly on a single product at a point in time. Because of the great variation across countries in both the range of drug compounds available and the dosage forms, strengths and pack sizes for each compound, obtaining a broadly comprehensive or representative sample is problematical. If products are required to match on all dimensions, including molecule, manufacturer, strength and pack, as is common in most international price comparisons, then only a very small and unrepresentative sample of the drugs available in each country can be included in the analysis. A trade-off between the desire to compare only identical products and the need to compare a truly representative sample of a country's pharmaceutical market is therefore necessary. A valid comparison of average drug prices should include generics and over-the-counter products that are good substitutes for branded prescription drugs, with all forms, strengths and packs. To achieve this broad representation, however, the requirements of same manufacturer, same brand, dosage form, strength and pack size must be

  12. Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm

    International Nuclear Information System (INIS)

    Yu, Lean; Wang, Shouyang; Lai, Kin Keung

    2008-01-01

    In this study, an empirical mode decomposition (EMD) based neural network ensemble learning paradigm is proposed for world crude oil spot price forecasting. For this purpose, the original crude oil spot price series were first decomposed into a finite, and often small, number of intrinsic mode functions (IMFs). Then a three-layer feed-forward neural network (FNN) model was used to model each of the extracted IMFs, so that the tendencies of these IMFs could be accurately predicted. Finally, the prediction results of all IMFs are combined with an adaptive linear neural network (ALNN), to formulate an ensemble output for the original crude oil price series. For verification and testing, two main crude oil price series, West Texas Intermediate (WTI) crude oil spot price and Brent crude oil spot price, are used to test the effectiveness of the proposed EMD-based neural network ensemble learning methodology. Empirical results obtained demonstrate attractiveness of the proposed EMD-based neural network ensemble learning paradigm. (author)

  13. Deregulated power prices: comparison of volatility

    International Nuclear Information System (INIS)

    Li Ying; Flynn, P.C.

    2004-01-01

    We examine electrical power price variability for 14 deregulated markets. Power price volatility is measured by price velocity, the daily average of the absolute value of price change per hour. Deregulated markets show a wide variability in price velocity. Some price velocity is expected and arises from the daily diurnal price pattern, which differs significantly between markets. Even when the expected daily variability in price is removed, the residual unexpected variability differs between markets. Some deregulated markets, most notably Britain and Spain, show patterns that are predictable and consistent and have low values of unexpected price velocity. These markets create a climate conducive to consumers facing the market through real time pricing and shaping consumption behaviors in response to price changes. Other markets, for example, South Australia and Alberta, have patterns that are inconsistent and irregular, and hence are hard for a customer to interpret; a customer in such a market will have a higher incentive to avoid demand side management and escape risk through hedging mechanisms

  14. International comparison of energy price using a purchasing power parity

    Energy Technology Data Exchange (ETDEWEB)

    Yoo, Dong Hun; Jo, Sung Han [Korea Energy Economics Institute, Euiwang (Korea)

    1999-05-01

    The price control of government results in price distortion as well as lowering efficiency of energy market and distortion of allocating resources. Consequently, such a price policy leads to energy overconsumption and has negative influences on other policies trying to reduce environmental burden resulted from energy consumption. When the energy price does not reflect the market price properly, it results in inefficiency of energy industry and it makes very difficult to supply investment funds. Therefore, the government is planning to implement liberalization of energy price step by step. The purpose of this study is to provide basic materials for establishing a reasonable energy price policy through the international comparison among OECD countries on major products price focusing on petroleum products. To overcome problems of exchange rate, a purchasing power indicator from OECD was used for comparative analysis with OECD countries. 11 refs. 1 fig., 23 tabs.

  15. Entity’s Irregular Demand Scheduling of the Wholesale Electricity Market based on the Forecast of Hourly Price Ratios

    Directory of Open Access Journals (Sweden)

    O. V. Russkov

    2015-01-01

    Full Text Available The article considers a hot issue to forecast electric power demand amounts and prices for the entities of wholesale electricity market (WEM, which are in capacity of a large user with production technology requirements prevailing over hourly energy planning ones. An electric power demand of such entities is on irregular schedule. The article analyses mathematical models, currently applied to forecast demand amounts and prices. It describes limits of time-series models and fundamental ones in case of hourly forecasting an irregular demand schedule of the electricity market entity. The features of electricity trading at WEM are carefully analysed. Factors that influence on irregularity of demand schedule of the metallurgical plant are shown. The article proposes method for the qualitative forecast of market price ratios as a tool to reduce a dependence on the accuracy of forecasting an irregular schedule of demand. It describes the differences between the offered method and the similar ones considered in research studies and scholarly works. The correlation between price ratios and relaxation in the requirements for the forecast accuracy of the electric power consumption is analysed. The efficiency function of forecast method is derived. The article puts an increased focus on description of the mathematical model based on the method of qualitative forecast. It shows main model parameters and restrictions the electricity market imposes on them. The model prototype is described as a programme module. Methods to assess an effectiveness of the proposed forecast model are examined. The positive test results of the model using JSC «Volzhsky Pipe Plant» data are given. A conclusion is drawn concerning the possibility to decrease dependence on the forecast accuracy of irregular schedule of entity’s demand at WEM. The effective trading tool has been found for the entities of irregular demand schedule at WEM. The tool application allows minimizing cost

  16. A computationally efficient electricity price forecasting model for real time energy markets

    International Nuclear Information System (INIS)

    Feijoo, Felipe; Silva, Walter; Das, Tapas K.

    2016-01-01

    Highlights: • A fast hybrid forecast model for electricity prices. • Accurate forecast model that combines K-means and machine learning techniques. • Low computational effort by elimination of feature selection techniques. • New benchmark results by using market data for year 2012 and 2015. - Abstract: Increased significance of demand response and proliferation of distributed energy resources will continue to demand faster and more accurate models for forecasting locational marginal prices. This paper presents such a model (named K-SVR). While yielding prediction accuracy comparable with the best known models in the literature, K-SVR requires a significantly reduced computational time. The computational reduction is attained by eliminating the use of a feature selection process, which is commonly used by the existing models in the literature. K-SVR is a hybrid model that combines clustering algorithms, support vector machine, and support vector regression. K-SVR is tested using Pennsylvania–New Jersey–Maryland market data from the periods 2005–6, 2011–12, and 2014–15. Market data from 2006 has been used to measure performance of many of the existing models. Authors chose these models to compare performance and demonstrate strengths of K-SVR. Results obtained from K-SVR using the market data from 2012 and 2015 are new, and will serve as benchmark for future models.

  17. Forecasting Crude Oil Price Using EEMD and RVM with Adaptive PSO-Based Kernels

    Directory of Open Access Journals (Sweden)

    Taiyong Li

    2016-12-01

    Full Text Available Crude oil, as one of the most important energy sources in the world, plays a crucial role in global economic events. An accurate prediction for crude oil price is an interesting and challenging task for enterprises, governments, investors, and researchers. To cope with this issue, in this paper, we proposed a method integrating ensemble empirical mode decomposition (EEMD, adaptive particle swarm optimization (APSO, and relevance vector machine (RVM—namely, EEMD-APSO-RVM—to predict crude oil price based on the “decomposition and ensemble” framework. Specifically, the raw time series of crude oil price were firstly decomposed into several intrinsic mode functions (IMFs and one residue by EEMD. Then, RVM with combined kernels was applied to predict target value for the residue and each IMF individually. To improve the prediction performance of each component, an extended particle swarm optimization (PSO was utilized to simultaneously optimize the weights and parameters of single kernels for the combined kernel of RVM. Finally, simple addition was used to aggregate all the predicted results of components into an ensemble result as the final result. Extensive experiments were conducted on the crude oil spot price of the West Texas Intermediate (WTI to illustrate and evaluate the proposed method. The experimental results are superior to those by several state-of-the-art benchmark methods in terms of root mean squared error (RMSE, mean absolute percent error (MAPE, and directional statistic (Dstat, showing that the proposed EEMD-APSO-RVM is promising for forecasting crude oil price.

  18. Cancer Drugs: An International Comparison of Postlicensing Price Inflation.

    Science.gov (United States)

    Savage, Philip; Mahmoud, Sarah; Patel, Yogin; Kantarjian, Hagop

    2017-06-01

    The cost of cancer drugs forms a rising proportion of health care budgets worldwide. A number of studies have examined international comparisons of initial cost, but there is little work on postlicensing price increases. To examine this, we compared cancer drug prices at initial sale and subsequent price inflation in the United States and United Kingdom and also reviewed relevant price control mechanisms. The 10 top-selling cancer drugs were selected, and their prices at initial launch and in 2015 were compared. Standard nondiscounted prices were obtained from the relevant annual copies of the RED BOOK and the British National Formulary. At initial marketing, prices were on average 42% higher in the United States than in the United Kingdom. After licensing in the United States, all 10 drugs had price rises averaging an overall annual 8.8% (range, 1.4% to 24.1%) increase. In comparison, in the United Kingdom, six drugs had unchanged prices, two had decreased prices, and two had modest price increases. The overall annual increase in the United Kingdom was 0.24%. Cancer drug prices are rising substantially, both at their initial marketing price and, in the United States, at postlicensing prices. In the United Kingdom, the Pharmaceutical Price Regulation Scheme, an agreement between the government and the pharmaceutical industry, controls health care costs while allowing a return on investment and funds for research. The increasing costs of cancer drugs are approaching the limits of sustainability, and a similar government-industry agreement may allow stability for both health care provision and the pharmaceutical industry in the United States.

  19. Modelling the Errors of EIA’s Oil Prices and Production Forecasts by the Grey Markov Model

    Directory of Open Access Journals (Sweden)

    Gholam Hossein Hasantash

    2012-01-01

    Full Text Available Grey theory is about systematic analysis of limited information. The Grey-Markov model can improve the accuracy of forecast range in the random fluctuating data sequence. In this paper, we employed this model in energy system. The average errors of Energy Information Administrations predictions for world oil price and domestic crude oil production from 1982 to 2007 and from 1985 to 2008 respectively were used as two forecasted examples. We showed that the proposed Grey-Markov model can improve the forecast accuracy of original Grey forecast model.

  20. Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

    Directory of Open Access Journals (Sweden)

    Yi Xiao

    2013-02-01

    Full Text Available Stock e-exchange prices forecasting is an important financial problem that is receiving increasing attention. This study proposes a novel three-stage nonlinear ensemble model. In the proposed model, three different types of neural-network based models, i.e. Elman network, generalized regression neural network (GRNN and wavelet neural network (WNN are constructed by three non-overlapping training sets and are further optimized by improved particle swarm optimization (IPSO. Finally, a neural-network-based nonlinear meta-model is generated by learning three neural-network based models through support vector machines (SVM neural network. The superiority of the proposed approach lies in its flexibility to account for potentially complex nonlinear relationships. Three daily stock indices time series are used for validating the forecasting model. Empirical results suggest the ensemble ANNs-PSO-GA approach can significantly improve the prediction performance over other individual models and linear combination models listed in this study.

  1. Deregulated power prices: comparison of diurnal patterns

    International Nuclear Information System (INIS)

    Ying Li; Flynn, P.C.

    2004-01-01

    We examine electrical power price, and in particular its daily and average weekday vs. weekend pattern of change, for 14 deregulated markets. Power price in deregulated markets shows fundamentally different patterns. North American markets show a monotonic diurnal weekday price pattern, while all other markets studied show more than one price peak. Deregulated power markets differ in maximum vs. minimum daily average price and in average weekday to weekend price, in turn creating a different incentive for a consumer to time shift power consuming activities. Markets differ in the extent to which a small fraction of the days shapes the average diurnal pattern and value of price. Deregulated markets show a wide variation in the correlation between load and price. Some deregulated markets, most notably Britain and Spain, show patterns that are predictable and consistent, and hence that can encourage a customer to shape consumption behaviors. Other markets, for example South Australia, have patterns that are inconsistent and irregular, and hence are hard for a customer to interpret; a customer in such a market will have a higher incentive to escape risk through hedging mechanisms. (Author)

  2. Deregulated power prices: comparison of diurnal patterns

    International Nuclear Information System (INIS)

    Li Ying; Flynn, Peter C.

    2004-01-01

    We examine electrical power price, and in particular its daily and average weekday vs. weekend pattern of change, for 14 deregulated markets. Power price in deregulated markets shows fundamentally different patterns. North American markets show a monotonic diurnal weekday price pattern, while all other markets studied show more than one price peak. Deregulated power markets differ in maximum vs. minimum daily average price and in average weekday to weekend price, in turn creating a different incentive for a consumer to time shift power consuming activities. Markets differ in the extent to which a small fraction of the days shapes the average diurnal pattern and value of price. Deregulated markets show a wide variation in the correlation between load and price. Some deregulated markets, most notably Britain and Spain, show patterns that are predictable and consistent, and hence that can encourage a customer to shape consumption behaviors. Other markets, for example South Australia, have patterns that are inconsistent and irregular, and hence are hard for a customer to interpret; a customer in such a market will have a higher incentive to escape risk through hedging mechanisms

  3. Price Comparisons on the Internet Based on Computational Intelligence

    Science.gov (United States)

    Kim, Jun Woo; Ha, Sung Ho

    2014-01-01

    Information-intensive Web services such as price comparison sites have recently been gaining popularity. However, most users including novice shoppers have difficulty in browsing such sites because of the massive amount of information gathered and the uncertainty surrounding Web environments. Even conventional price comparison sites face various problems, which suggests the necessity of a new approach to address these problems. Therefore, for this study, an intelligent product search system was developed that enables price comparisons for online shoppers in a more effective manner. In particular, the developed system adopts linguistic price ratings based on fuzzy logic to accommodate user-defined price ranges, and personalizes product recommendations based on linguistic product clusters, which help online shoppers find desired items in a convenient manner. PMID:25268901

  4. Forecasting of palm oil price in Malaysia using linear and nonlinear methods

    Science.gov (United States)

    Nor, Abu Hassan Shaari Md; Sarmidi, Tamat; Hosseinidoust, Ehsan

    2014-09-01

    The first question that comes to the mind is: "How can we predict the palm oil price accurately?" This question is the authorities, policy makers and economist's question for a long period of time. The first reason is that in the recent years Malaysia showed a comparative advantage in palm oil production and has become top producer and exporter in the world. Secondly, palm oil price plays significant role in government budget and represents important source of income for Malaysia, which potentially can influence the magnitude of monetary policies and eventually have an impact on inflation. Thirdly, knowledge on the future trends would be helpful in the planning and decision making procedures and will generate precise fiscal and monetary policy. Daily data on palm oil prices along with the ARIMA models, neural networks and fuzzy logic systems are employed in this paper. Empirical findings indicate that the dynamic neural network of NARX and the hybrid system of ANFIS provide higher accuracy than the ARIMA and static neural network for forecasting the palm oil price in Malaysia.

  5. Modeling and forecasting foreign exchange daily closing prices with normal inverse Gaussian

    Science.gov (United States)

    Teneng, Dean

    2013-09-01

    We fit the normal inverse Gaussian(NIG) distribution to foreign exchange closing prices using the open software package R and select best models by Käärik and Umbleja (2011) proposed strategy. We observe that daily closing prices (12/04/2008 - 07/08/2012) of CHF/JPY, AUD/JPY, GBP/JPY, NZD/USD, QAR/CHF, QAR/EUR, SAR/CHF, SAR/EUR, TND/CHF and TND/EUR are excellent fits while EGP/EUR and EUR/GBP are good fits with a Kolmogorov-Smirnov test p-value of 0.062 and 0.08 respectively. It was impossible to estimate normal inverse Gaussian parameters (by maximum likelihood; computational problem) for JPY/CHF but CHF/JPY was an excellent fit. Thus, while the stochastic properties of an exchange rate can be completely modeled with a probability distribution in one direction, it may be impossible the other way around. We also demonstrate that foreign exchange closing prices can be forecasted with the normal inverse Gaussian (NIG) Lévy process, both in cases where the daily closing prices can and cannot be modeled by NIG distribution.

  6. A Study on the Determination of the World Crude Oil Price and Methods for Its Forecast

    Energy Technology Data Exchange (ETDEWEB)

    Kim, J.K. [Korea Energy Economics Institute, Euiwang (Korea)

    2001-11-01

    The primary purpose of this report is to provide the groundwork to develop the methods to forecast the world crude oil price. The methodology is used by both literature survey and empirical study. For this purpose, first of all, this report reviewed the present situation and the outlook of the world oil market based on oil demand, supply and prices. This analysis attempted to provide a deeper understanding to support the development of oil forecasting methods. The result of this review, in general, showed that the oil demand will be maintained annually at an average rate of around 2.4% under assumption that oil supply has no problem until 2020. The review showed that crude oil price will be a 3% increasing rate annually in the 1999 real term. This report used the contents of the summary review as reference data in order to link the KEEIOF model. In an effort to further investigate the contents of oil political economy, this report reviewed the articles of political economy about oil industry. It pointed out that the world oil industry is experiencing the change of restructuring oil industry after the Gulf War in 1990. The contents of restructuring oil industry are characterized by the 'open access' to resources not only in the Persian Gulf, but elsewhere in the world as well - especially the Caspian Sea Basin. In addition, the contents showed that the oil industries are shifted from government control to government and industry cooperation after the Gulf War. In order to examine the characters and the problems surrounding oil producing countries, this report described the model of OPEC behavior and strategy of oil management with political and military factors. Among examining the models of OPEC behavior, this report focused on hybrid model to explain OPEC behavior. In reviewing political and religious power structure in the Middle East, the report revealed that US emphasizes the importance of the Middle East for guaranteeing oil security. However, three

  7. Electricity Price Forecast Using Combined Models with Adaptive Weights Selected and Errors Calibrated by Hidden Markov Model

    Directory of Open Access Journals (Sweden)

    Da Liu

    2013-01-01

    Full Text Available A combined forecast with weights adaptively selected and errors calibrated by Hidden Markov model (HMM is proposed to model the day-ahead electricity price. Firstly several single models were built to forecast the electricity price separately. Then the validation errors from every individual model were transformed into two discrete sequences: an emission sequence and a state sequence to build the HMM, obtaining a transmission matrix and an emission matrix, representing the forecasting ability state of the individual models. The combining weights of the individual models were decided by the state transmission matrixes in HMM and the best predict sample ratio of each individual among all the models in the validation set. The individual forecasts were averaged to get the combining forecast with the weights obtained above. The residuals of combining forecast were calibrated by the possible error calculated by the emission matrix of HMM. A case study of day-ahead electricity market of Pennsylvania-New Jersey-Maryland (PJM, USA, suggests that the proposed method outperforms individual techniques of price forecasting, such as support vector machine (SVM, generalized regression neural networks (GRNN, day-ahead modeling, and self-organized map (SOM similar days modeling.

  8. The Impact of Price Comparison Service on Pricing Strategy in a Dual-Channel Supply Chain

    Directory of Open Access Journals (Sweden)

    Qi Xu

    2013-01-01

    Full Text Available Recently, price comparison service (PCS websites are more and more popular due to its features in facilitating transparent price and promoting rational purchase decision. Motivated by the industrial practices, in this study, we examine the pricing strategies of retailers and supplier in a dual-channel supply chain influenced by the signals of PCS. We categorize and discuss three situations according to the signal availability of PCS, under which the optimal pricing strategies are derived. Finally, we conduct a numerical study and find that in fact the retailers and supplier are all more willing to avoid the existence of PCS with the objective of profit maximization. When both of retailers are affected by the PCS, the supplier is more willing to reduce the availability of price information. Important managerial insights are discussed.

  9. Distributional modeling and short-term forecasting of electricity prices by Generalized Additive Models for Location, Scale and Shape

    International Nuclear Information System (INIS)

    Serinaldi, Francesco

    2011-01-01

    In the context of the liberalized and deregulated electricity markets, price forecasting has become increasingly important for energy company's plans and market strategies. Within the class of the time series models that are used to perform price forecasting, the subclasses of methods based on stochastic time series and causal models commonly provide point forecasts, whereas the corresponding uncertainty is quantified by approximate or simulation-based confidence intervals. Aiming to improve the uncertainty assessment, this study introduces the Generalized Additive Models for Location, Scale and Shape (GAMLSS) to model the dynamically varying distribution of prices. The GAMLSS allow fitting a variety of distributions whose parameters change according to covariates via a number of linear and nonlinear relationships. In this way, price periodicities, trends and abrupt changes characterizing both the position parameter (linked to the expected value of prices), and the scale and shape parameters (related to price volatility, skewness, and kurtosis) can be explicitly incorporated in the model setup. Relying on the past behavior of the prices and exogenous variables, the GAMLSS enable the short-term (one-day ahead) forecast of the entire distribution of prices. The approach was tested on two datasets from the widely studied California Power Exchange (CalPX) market, and the less mature Italian Power Exchange (IPEX). CalPX data allow comparing the GAMLSS forecasting performance with published results obtained by different models. The study points out that the GAMLSS framework can be a flexible alternative to several linear and nonlinear stochastic models. - Research Highlights: ► Generalized Additive Models for Location, Scale and Shape (GAMLSS) are used to model electricity prices' time series. ► GAMLSS provide the entire dynamicaly varying distribution function of prices resorting to a suitable set of covariates that drive the instantaneous values of the parameters

  10. Day-Ahead Crude Oil Price Forecasting Using a Novel Morphological Component Analysis Based Model

    Directory of Open Access Journals (Sweden)

    Qing Zhu

    2014-01-01

    Full Text Available As a typical nonlinear and dynamic system, the crude oil price movement is difficult to predict and its accurate forecasting remains the subject of intense research activity. Recent empirical evidence suggests that the multiscale data characteristics in the price movement are another important stylized fact. The incorporation of mixture of data characteristics in the time scale domain during the modelling process can lead to significant performance improvement. This paper proposes a novel morphological component analysis based hybrid methodology for modeling the multiscale heterogeneous characteristics of the price movement in the crude oil markets. Empirical studies in two representative benchmark crude oil markets reveal the existence of multiscale heterogeneous microdata structure. The significant performance improvement of the proposed algorithm incorporating the heterogeneous data characteristics, against benchmark random walk, ARMA, and SVR models, is also attributed to the innovative methodology proposed to incorporate this important stylized fact during the modelling process. Meanwhile, work in this paper offers additional insights into the heterogeneous market microstructure with economic viable interpretations.

  11. Multi-step ahead forecasts for electricity prices using NARX: A new approach, a critical analysis of one-step ahead forecasts

    International Nuclear Information System (INIS)

    Andalib, Arash; Atry, Farid

    2009-01-01

    The prediction of electricity prices is very important to participants of deregulated markets. Among many properties, a successful prediction tool should be able to capture long-term dependencies in market's historical data. A nonlinear autoregressive model with exogenous inputs (NARX) has proven to enjoy a superior performance to capture such dependencies than other learning machines. However, it is not examined for electricity price forecasting so far. In this paper, we have employed a NARX network for forecasting electricity prices. Our prediction model is then compared with two currently used methods, namely the multivariate adaptive regression splines (MARS) and wavelet neural network. All the models are built on the reconstructed state space of market's historical data, which either improves the results or decreases the complexity of learning algorithms. Here, we also criticize the one-step ahead forecasts for electricity price that may suffer a one-term delay and we explain why the mean square error criterion does not guarantee a functional prediction result in this case. To tackle the problem, we pursue multi-step ahead predictions. Results for the Ontario electricity market are presented

  12. Crude Oil Price Forecasting Based on Hybridizing Wavelet Multiple Linear Regression Model, Particle Swarm Optimization Techniques, and Principal Component Analysis

    Science.gov (United States)

    Shabri, Ani; Samsudin, Ruhaidah

    2014-01-01

    Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series. PMID:24895666

  13. Crude Oil Price Forecasting Based on Hybridizing Wavelet Multiple Linear Regression Model, Particle Swarm Optimization Techniques, and Principal Component Analysis

    Directory of Open Access Journals (Sweden)

    Ani Shabri

    2014-01-01

    Full Text Available Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI, has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.

  14. Crude oil price forecasting based on hybridizing wavelet multiple linear regression model, particle swarm optimization techniques, and principal component analysis.

    Science.gov (United States)

    Shabri, Ani; Samsudin, Ruhaidah

    2014-01-01

    Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.

  15. On the importance of the long-term seasonal component in day-ahead electricity price forecasting

    International Nuclear Information System (INIS)

    Nowotarski, Jakub; Weron, Rafał

    2016-01-01

    In day-ahead electricity price forecasting (EPF) the daily and weekly seasonalities are always taken into account, but the long-term seasonal component (LTSC) is believed to add unnecessary complexity to the already parameter-rich models and is generally ignored. Conducting an extensive empirical study involving state-of-the-art time series models we show that (i) decomposing a series of electricity prices into a LTSC and a stochastic component, (ii) modeling them independently and (iii) combining their forecasts can bring – contrary to a common belief – an accuracy gain compared to an approach in which a given time series model is calibrated to the prices themselves. - Highlights: • A new class of Seasonal Component AutoRegressive (SCAR) models is introduced. • Electricity prices are decomposed into a trend-seasonal and a stochastic component. • Both components are modeled independently, their forecasts are combined. • Significant accuracy gains can be achieved compared to commonly used approaches.

  16. On maximizing profit of wind-battery supported power station based on wind power and energy price forecasting

    DEFF Research Database (Denmark)

    Khalid, Muhammad; Aguilera, Ricardo P.; Savkin, Andrey V.

    2017-01-01

    This paper proposes a framework to develop an optimal power dispatch strategy for grid-connected wind power plants containing a Battery Energy Storage System (BESS). Considering the intermittent nature of wind power and rapidly varying electricity market price, short-term forecasting...... Dynamic Programming tool which can incorporate the predictions of both wind power and market price simultaneously as inputs in a receding horizon approach. The proposed strategy is validated using real electricity market price and wind power data in different scenarios of BESS power and capacity...... of these variables is used for efficient energy management. The predicted variability trends in market price assist in earning additional income which subsequently increase the operational profit. Then on the basis of income improvement, optimal capacity of the BESS can be determined. The proposed framework utilizes...

  17. Apples with apples: accounting for fuel price risk in comparisons of gas-fired and renewable generation

    Energy Technology Data Exchange (ETDEWEB)

    Bolinger, Mark; Wiser, Ryan

    2003-12-18

    For better or worse, natural gas has become the fuel of choice for new power plants being built across the United States. According to the US Energy Information Administration (EIA), natural gas combined-cycle and combustion turbine power plants accounted for 96% of the total generating capacity added in the US between 1999 and 2002--138 GW out of a total of 144 GW. Looking ahead, the EIA expects that gas-fired technology will account for 61% of the 355 GW new generating capacity projected to come on-line in the US up to 2025, increasing the nationwide market share of gas-fired generation from 18% in 2002 to 22% in 2025. While the data are specific to the US, natural gas-fired generation is making similar advances in other countries as well. Regardless of the explanation for (or interpretation of) the empirical findings, however, the basic implications remain the same: one should not blindly rely on gas price forecasts when comparing fixed-price renewable with variable-price gas-fired generation contracts. If there is a cost to hedging, gas price forecasts do not capture and account for it. Alternatively, if the forecasts are at risk of being biased or out of tune with the market, then one certainly would not want to use them as the basis for resource comparisons or investment decisions if a more certain source of data (forwards) existed. Accordingly, assuming that long-term price stability is valued, the most appropriate way to compare the levelized cost of these resources in both cases would be to use forward natural gas price data--i.e. prices that can be locked in to create price certainty--as opposed to uncertain natural gas price forecasts. This article suggests that had utilities and analysts in the US done so over the sample period from November 2000 to November 2003, they would have found gas-fired generation to be at least 0.3-0.6 cents/kWh more expensive (on a levelized cost basis) than otherwise thought. With some renewable resources, in particular wind

  18. On the electrification of road transport - Learning rates and price forecasts for hybrid-electric and battery-electric vehicles

    International Nuclear Information System (INIS)

    Weiss, Martin; Patel, Martin K.; Junginger, Martin; Perujo, Adolfo; Bonnel, Pierre; Grootveld, Geert van

    2012-01-01

    Hybrid-electric vehicles (HEVs) and battery-electric vehicles (BEVs) are currently more expensive than conventional passenger cars but may become cheaper due to technological learning. Here, we obtain insight into the prospects of future price decline by establishing ex-post learning rates for HEVs and ex-ante price forecasts for HEVs and BEVs. Since 1997, HEVs have shown a robust decline in their price and price differential at learning rates of 7±2% and 23±5%, respectively. By 2010, HEVs were only 31±22 € 2010 kW −1 more expensive than conventional cars. Mass-produced BEVs are currently introduced into the market at prices of 479±171 € 2010 kW −1 , which is 285±213 € 2010 kW −1 and 316±209 € 2010 kW −1 more expensive than HEVs and conventional cars. Our forecast suggests that price breakeven with these vehicles may only be achieved by 2026 and 2032, when 50 and 80 million BEVs, respectively, would have been produced worldwide. We estimate that BEVs may require until then global learning investments of 100–150 billion € which is less than the global subsidies for fossil fuel consumption paid in 2009. These findings suggest that HEVs, including plug-in HEVs, could become the dominant vehicle technology in the next two decades, while BEVs may require long-term policy support. - Highlights: ► Learning rates for hybrid-electric and battery-electric vehicles. ► Prices and price differentials of hybrid-electric vehicles show a robust decline. ► Battery-electric vehicles may require policy support for decades.

  19. Forecasting Electricity Market Price for End Users in EU28 until 2020—Main Factors of Influence

    Directory of Open Access Journals (Sweden)

    Simon Pezzutto

    2018-06-01

    Full Text Available The scope of the present investigation is to provide a description of final electricity prices development in the context of deregulated electricity markets in EU28, up to 2020. We introduce a new methodology to predict long-term electricity market prices consisting of two parts: (1 a self-developed form of Porter’s five forces analysis (PFFA determining that electricity markets are characterized by a fairly steady price increase. Dominant driving factors come out to be: (i uncertainty of future electricity prices; (ii regulatory complexity; and (iii generation overcapacities. Similar conclusions derive from (2 a self-developed form of multiple-criteria decision analysis (MCDA. In this case, we find that the electricity market particularly depends on (i market liberalization and (ii the European Union (EU’s economy growth. The applied methodologies provide a novel contribution in forecasting electricity price trends, by analyzing the sentiments, expectations, and knowledge of industry experts, through an assessment of factors influencing the market price and goals of key market participants. An extensive survey was conducted, interviewing experts all over Europe showed that the electricity market is subject to a future slight price increase.

  20. Effects of the uncertainty of energy price and water availability forecasts on the operation of Alpine hydropower reservoir systems

    Science.gov (United States)

    Anghileri, D.; Castelletti, A.; Burlando, P.

    2016-12-01

    European energy markets have experienced dramatic changes in the last years because of the massive introduction of Variable Renewable Sources (VRSs), such as wind and solar power sources, in the generation portfolios in many countries. VRSs i) are intermittent, i.e., their production is highly variable and only partially predictable, ii) are characterized by no correlation between production and demand, iii) have negligible costs of production, and iv) have been largely subsidized. These features result in lower energy prices, but, at the same time, in increased price volatility, and in network stability issues, which pose a threat to traditional power sources because of smaller incomes and higher maintenance costs associated to a more flexible operation of power systems. Storage hydropower systems play an important role in compensating production peaks, both in term of excess and shortage of energy. Traditionally, most of the research effort in hydropower reservoir operation has focused on modeling and forecasting reservoir inflow as well as designing reservoir operation accordingly. Nowadays, price variability may be the largest source of uncertainty in the context of hydropower systems, especially when considering medium-to-large reservoirs, whose storage can easily buffer small inflow fluctuations. In this work, we compare the effects of uncertain inflow and energy price forecasts on hydropower production and profitability. By adding noise to historic inflow and price trajectories, we build a set of synthetic forecasts corresponding to different levels of predictability and assess their impact on reservoir operating policies and performances. The study is conducted on different hydropower systems, including storage systems and pumped-storage systems, with different characteristics, e.g., different inflow-capacity ratios. The analysis focuses on Alpine hydropower systems where the hydrological regime ranges from purely ice and snow-melt dominated to mixed snow

  1. Comparison of control systems for the optimisation of ice storage in a dynamic real time electricity pricing environment

    International Nuclear Information System (INIS)

    Murphy, M.D.; O’Mahony, M.J.; Upton, J.

    2015-01-01

    forecasted day ahead real time electricity pricing environment. The energy savings achieved for 100% ice bank charge were low, but as the desired charge level was reduced to 75%, 50% and 25% the savings potential increased. The introduction of uncertain real time electricity pricing forecasts nullified any cost savings made by the load-shifting controller in comparison to the current state of the art

  2. Mid-Term Electricity Market Clearing Price Forecasting with Sparse Data: A Case in Newly-Reformed Yunnan Electricity Market

    Directory of Open Access Journals (Sweden)

    Chuntian Cheng

    2016-10-01

    Full Text Available For the power systems, for which few data are available for mid-term electricity market clearing price (MCP forecasting at the early stage of market reform, a novel grey prediction model (defined as interval GM(0, N model is proposed in this paper. Over the traditional GM(0, N model, three major improvements of the proposed model are: (i the lower and upper bounds are firstly identified to give an interval estimation of the forecasting value; (ii a novel whitenization method is then established to determine the definite forecasting value from the forecasting interval; and (iii the model parameters are identified by an improved particle swarm optimization (PSO instead of the least square method (LSM for the limitation of LSM. Finally, a newly-reformed electricity market in Yunnan province of China is studied, and input variables are contrapuntally selected. The accuracy of the proposed model is validated by observed data. Compared with the multiple linear regression (MLR model, the traditional GM(0, N model and the artificial neural network (ANN model, the proposed model gives a better performance and its superiority is further ensured by the use of the modified Diebold–Mariano (MDM test, suggesting that it is suitable for mid-term electricity MCP forecasting in a data-sparse electricity market.

  3. Energy price comparison of new, renewable, and fossil energy sources

    International Nuclear Information System (INIS)

    Edwaren Liun; Sunardi

    2014-01-01

    Low cost transportation for people and goods is essential to the economic well-being of the nation. Until now, if the oil prices rise, the cost of transportation will automatically follow and most of the people suffering due to soaring prices of food and other items. Almost 100 percent of Indonesian transportation energy demand is supported by oil. Supply disruption - or even the threat of disruption - in the Middle East or elsewhere may lead to a shift in consumer prices and the cost of the industry in significant numbers. While costs in the energy sector, especially electricity in developed countries that also contribute significantly to support the transport sector, is much more stable and predictable. Energy requirements are so high in the transport sector tends to force people to seek the source and means of energy in other forms such as electricity or hydrogen that can match or exceed the performance of fuel oil. This paper aims to analyze the economics of energy price comparison to see the extent of the economic opportunities some kind of energy to play a significant role in the transport sector and the subsequent impact on the energy system. From the results obtained by the analysis that will be increasingly necessary role of nuclear energy and other specific energy as a source of electrical energy considering its economical aspects are relatively better. (author)

  4. A Hybrid Forecasting Model Based on Bivariate Division and a Backpropagation Artificial Neural Network Optimized by Chaos Particle Swarm Optimization for Day-Ahead Electricity Price

    Directory of Open Access Journals (Sweden)

    Zhilong Wang

    2014-01-01

    Full Text Available In the electricity market, the electricity price plays an inevitable role. Nevertheless, accurate price forecasting, a vital factor affecting both government regulatory agencies and public power companies, remains a huge challenge and a critical problem. Determining how to address the accurate forecasting problem becomes an even more significant task in an era in which electricity is increasingly important. Based on the chaos particle swarm optimization (CPSO, the backpropagation artificial neural network (BPANN, and the idea of bivariate division, this paper proposes a bivariate division BPANN (BD-BPANN method and the CPSO-BD-BPANN method for forecasting electricity price. The former method creatively transforms the electricity demand and price to be a new variable, named DV, which is calculated using the division principle, to forecast the day-ahead electricity by multiplying the forecasted values of the DVs and forecasted values of the demand. Next, to improve the accuracy of BD-BPANN, chaos particle swarm optimization and BD-BPANN are synthesized to form a novel model, CPSO-BD-BPANN. In this study, CPSO is utilized to optimize the initial parameters of BD-BPANN to make its output more stable than the original model. Finally, two forecasting strategies are proposed regarding different situations.

  5. Optimal decisions and comparison of VMI and CPFR under price-sensitive uncertain demand

    Directory of Open Access Journals (Sweden)

    Yasaman Kazemi

    2013-06-01

    Full Text Available Purpose: The purpose of this study is to compare the performance of two advanced supply chain coordination mechanisms, Vendor Managed Inventory (VMI and Collaborative Planning Forecasting and Replenishment (CPFR, under a price-sensitive uncertain demand environment, and to make the optimal decisions on retail price and order quantity for both mechanisms. Design/ methodology/ approach: Analytical models are first applied to formulate a profit maximization problem; furthermore, by applying simulation optimization solution procedures, the optimal decisions and performance comparisons are accomplished. Findings: The results of the case study supported the widely held view that more advanced coordination mechanisms yield greater supply chain profit than less advanced ones. Information sharing does not only increase the supply chain profit, but also is required for the coordination mechanisms to achieve improved performance. Research limitations/implications: This study considers a single vendor and a single retailer in order to simplify the supply chain structure for modeling. Practical implications: Knowledge obtained from this study about the conditions appropriate for each specific coordination mechanism and the exact functions of coordination programs is critical to managerial decisions for industry practitioners who may apply the coordination mechanisms considered. Originality/value: This study includes the production cost in Economic Order Quantity (EOQ equations and combines it with price-sensitive demand under stochastic settings while comparing VMI and CPFR supply chain mechanisms and maximizing the total profit. Although many studies have worked on information sharing within the supply chain, determining the performance measures when the demand is price-sensitive and stochastic was not reported by researchers in the past literature.

  6. The Carbon Trading Price and Trading Volume Forecast in Shanghai City by BP Neural Network

    OpenAIRE

    Liu Zhiyuan; Sun Zongdi

    2017-01-01

    In this paper, the BP neural network model is established to predict the carbon trading price and carbon trading volume in Shanghai City. First of all, we find the data of carbon trading price and carbon trading volume in Shanghai City from September 30, 2015 to December 23, 2016. The carbon trading price and trading volume data were processed to get the average value of each 5, 10, 20, 30, and 60 carbon trading price and trading volume. Then, these data are used as input of BP neural network...

  7. 16 CFR 233.1 - Former price comparisons.

    Science.gov (United States)

    2010-01-01

    ... each. His usual markup is 50 percent over cost; that is, his regular retail price is $7.50. In order... that the former price is not a fictitious one. If the former price, or the amount or percentage of...

  8. OSSIM wave-optics toolbox and its use to simulate AEOS

    Science.gov (United States)

    Smith, Carey A.; Forgham, James L.; Jones, Bruce W.; Jones, Kenneth D.

    2001-12-01

    OSSim (Optical System Simulation) is a simulation toolbox of optical and processing components. By using full wave-optics in the time-domain, OSSim simulates diffractive effects and control loop interactions missed by simpler analyses. OSSim also models the atmosphere, with user customizable turbulence strength, wind, and slew. This paper first presents 2 introductory examples: a simple 2-lens imaging system and a simple tilt-control system. Then it presents a simulation of the 3.67-meter AEOS (Advanced Electro-Optics System) telescope on Maui. The OSSim simulation agrees well with the AEOS experimental results.

  9. NewsMarket 2.0: Analysis of News for Stock Price Forecasting

    Science.gov (United States)

    Barazzetti, Alessandro; Mastronardi, Rosangela

    Most of the existing financial research tools use a stock's historical price and technical indicators to predict future price trends without taking into account the impact of web news. The recent explosion of demand for information on financial investment management is driving the search for alternative methods of quantitative data analysis.

  10. Application of Neural Network Technologies for Price Forecasting in the Liberalized Electricity Market

    Science.gov (United States)

    Gerikh, Valentin; Kolosok, Irina; Kurbatsky, Victor; Tomin, Nikita

    2009-01-01

    The paper presents the results of experimental studies concerning calculation of electricity prices in different price zones in Russia and Europe. The calculations are based on the intelligent software "ANAPRO" that implements the approaches based on the modern methods of data analysis and artificial intelligence technologies.

  11. An analysis of the oil prices: stationary and forecasting models; Uma analise dos precos do petroleo no mercado internacional: estacionaridade e modelos de previsao

    Energy Technology Data Exchange (ETDEWEB)

    Salles, Andre A. de; Veiga, Iago E. B. da Costa; Machado, Rafael G.T. [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil)

    2008-07-01

    The movements of the oil prices in the international market are important for any planning, so the study of this variable is relevant for the investment and financing decisions of the production. The purpose of this work is to study the time series of the quotations of the spot prices of the crude oil in the international market. The objectives of this work are to study the movements of time series of the prices, and the returns, of the crude oil prices gives emphasis in the stationary. The other focus of this work is to develop forecasting models for the oil prices, or the returns of the oil prices. The selected sample was of the daily quotation of the prices of types WTI and Brent, for the period from January 2005 to April 2007. (author)

  12. Medium-Term Probabilistic Forecasting of Extremely Low Prices in Electricity Markets: Application to the Spanish Case

    Directory of Open Access Journals (Sweden)

    Antonio Bello

    2016-03-01

    Full Text Available One of the most relevant challenges that have arisen in electricity markets during the last few years is the emergence of extremely low prices. Trying to predict these events is crucial for market agents in a competitive environment. This paper proposes a novel methodology to simultaneously accomplish punctual and probabilistic hourly predictions about the appearance of extremely low electricity prices in a medium-term scope. The proposed approach for making real ex ante forecasts consists of a nested compounding of different forecasting techniques, which incorporate Monte Carlo simulation, combined with spatial interpolation techniques. The procedure is based on the statistical identification of the process key drivers. Logistic regression for rare events, decision trees, multilayer perceptrons and a hybrid approach, which combines a market equilibrium model with logistic regression, are used. Moreover, this paper assesses whether periodic models in which parameters switch according to the day of the week can be even more accurate. The proposed techniques are compared to a Markov regime switching model and several naive methods. The proposed methodology empirically demonstrates its effectiveness by achieving promising results on a real case study based on the Spanish electricity market. This approach can provide valuable information for market agents when they face decision making and risk-management processes. Our findings support the additional benefit of using a hybrid approach for deriving more accurate predictions.

  13. The Use of Neural Network and Portfolio Analysis in Forecasting Share Prices at the Stock Exchange

    Directory of Open Access Journals (Sweden)

    Przemyslaw Stochel

    2000-01-01

    Full Text Available The article presents the use of neural networks in decision making process on the capital market. The author tried to show the efficiency of established solution in Polish reality which features different conditions in comparison with the markets of higher developed countries. The aim of the paper was to prove that neural networks are flexible tools which on one hand might be adjusted to investor's requirements and on the other, can reduce equirements to his experience. The article is based on the author's own research carried out by modelling neural network operation with a simulation program. The established solutions are input which employs stocks portfolio computed on the basis of Markowitz portfolio theory and Sharpe's model. According to the established propositions, the portfolio created in such a way is modified by neutral network in order to optimise a criterion which maximises the income of such a modified portfolio. A detailed genesis of the established input vector and network structure are presented. It allows the reader to carry out his own research and create his own attitude towards applied values. The research results based on a real stock market database with the use of one-output networks predicting thc price of a single company - Agros as well as networks predicting the desirable structure of the whole portfolio are presented. The effect of the network structure leaming parameters, input vector (not only as to the input quantity but also as to period of time they were collected was examined. The dependence between the factors mentioned above such as input vector and network structure were discussed. lt seems that the presented paper has proved that some not widely spread methods with neural networks can become at competitive tool to optimisation methods.

  14. Long range dependency and forecasting of housing price index and mortgage market rate: evidence of subprime crisis

    Directory of Open Access Journals (Sweden)

    Nadhem Selmi

    2015-05-01

    Full Text Available In this paper, we examine and forecast the House Price Index (HPI and mortgage market rate in terms of the description of the subprime crisis. We use a semi-parametric local polynomial Whittle estimator proposed by Shimotsu et al. (2005 [Shimotsu, K., & Phillips, P.C.B. (2005, Exact local Whittle estimation of fractional integration. The Annals of Statistics, 33(4, 1890-1933.] in a long memory parameter time series. Empirical investigation of HPI and mortgage market rate shows that these variables are more persistent when the d estimates are found on the Shimotsu method than on the one of Künsch (1987 [Künsch, H.R. (1987. Statistical aspects of self-similar processes. In Y. Prokhorov and V.V. Sazanov (eds., Proceedings of the First World Congress of the Bernoulli Society, VNU Science Press, Utrecht, 67-74.]. The estimating forecast values are more realistic and they strongly reflect the present US economy actuality in the two series as indicated by the forecast evaluation topics.

  15. The Interval Slope Method for Long-Term Forecasting of Stock Price Trends

    Directory of Open Access Journals (Sweden)

    Chun-xue Nie

    2016-01-01

    Full Text Available A stock price is a typical but complex type of time series data. We used the effective prediction of long-term time series data to schedule an investment strategy and obtain higher profit. Due to economic, environmental, and other factors, it is very difficult to obtain a precise long-term stock price prediction. The exponentially segmented pattern (ESP is introduced here and used to predict the fluctuation of different stock data over five future prediction intervals. The new feature of stock pricing during the subinterval, named the interval slope, can characterize fluctuations in stock price over specific periods. The cumulative distribution function (CDF of MSE was compared to those of MMSE-BC and SVR. We concluded that the interval slope developed here can capture more complex dynamics of stock price trends. The mean stock price can then be predicted over specific time intervals relatively accurately, in which multiple mean values over time intervals are used to express the time series in the long term. In this way, the prediction of long-term stock price can be more precise and prevent the development of cumulative errors.

  16. Determining the best forecasting method to estimate unitary charges price indexes of PFI data in central region Peninsular Malaysia

    Science.gov (United States)

    Ahmad Kamaruddin, Saadi Bin; Md Ghani, Nor Azura; Mohamed Ramli, Norazan

    2013-04-01

    The concept of Private Financial Initiative (PFI) has been implemented by many developed countries as an innovative way for the governments to improve future public service delivery and infrastructure procurement. However, the idea is just about to germinate in Malaysia and its success is still vague. The major phase that needs to be given main attention in this agenda is value for money whereby optimum efficiency and effectiveness of each expense is attained. Therefore, at the early stage of this study, estimating unitary charges or materials price indexes in each region in Malaysia was the key objective. This particular study aims to discover the best forecasting method to estimate unitary charges price indexes in construction industry by different regions in the central region of Peninsular Malaysia (Selangor, Federal Territory of Kuala Lumpur, Negeri Sembilan, and Melaka). The unitary charges indexes data used were from year 2002 to 2011 monthly data of different states in the central region Peninsular Malaysia, comprising price indexes of aggregate, sand, steel reinforcement, ready mix concrete, bricks and partition, roof material, floor and wall finishes, ceiling, plumbing materials, sanitary fittings, paint, glass, steel and metal sections, timber and plywood. At the end of the study, it was found that Backpropagation Neural Network with linear transfer function produced the most accurate and reliable results for estimating unitary charges price indexes in every states in central region Peninsular Malaysia based on the Root Mean Squared Errors, where the values for both estimation and evaluation sets were approximately zero and highly significant at p Malaysia. The estimated price indexes of construction materials will contribute significantly to the value for money of PFI as well as towards Malaysian economical growth.

  17. Density forecasts of crude-oil prices using option-implied and ARCH-type models

    DEFF Research Database (Denmark)

    Høg, Esben; Tsiaras, Leonicas

    2011-01-01

    of derivative contracts. Risk-neutral densities, obtained from panels of crude-oil option prices, are adjusted to reflect real-world risks using either a parametric or a non-parametric calibration approach. The relative performance of the models is evaluated for the entire support of the density, as well...... obtained by option prices and non-parametric calibration methods over those constructed using historical returns and simulated ARCH processes. © 2010 Wiley Periodicals, Inc. Jrl Fut Mark...

  18. Extension of the new proposed association equation of state (AEOS) to associating fluid mixtures

    International Nuclear Information System (INIS)

    Rezaei, H.; Modarress, H.; Mohsen-Nia, M.

    2010-01-01

    Recently, a new statistical mechanic-based equation of state has been proposed by Mohsen-Nia and Modarress [M. Mohsen-Nia, H. Modarress, Chem. Phys. 336 (2007) 22-26] for associating pure fluids. The new association equation of state (AEOS) was successfully applied to calculate the saturated properties of water, methanol, and ammonia. In this work, the new proposed AEOS is used to evaluate the (vapour + liquid) equilibrium (VLE) of 25 associating pure compounds and the adjusted parameters are reported. The new AEOS is also extended to mixtures containing associating and non-associating compounds. The calculated saturated properties of the pure compounds are compared with those calculated by other AEOSs. The results of VLE calculation for various binary mixtures such as: alcohol/hydrocarbon, alcohol/CO 2 , alcohol/aromatic-hydrocarbons, and the quaternary system (H 2 O/CH 4 /CO 2 /H 2 S) indicate the capability of the new proposed AEOS for associating pure and mixture calculations.

  19. Market data analysis and short-term price forecasting in the Iran electricity market with pay-as-bid payment mechanism

    International Nuclear Information System (INIS)

    Bigdeli, N.; Afshar, K.; Amjady, N.

    2009-01-01

    Market data analysis and short-term price forecasting in Iran electricity market as a market with pay-as-bid payment mechanism has been considered in this paper. The data analysis procedure includes both correlation and predictability analysis of the most important load and price indices. The employed data are the experimental time series from Iran electricity market in its real size and is long enough to make it possible to take properties such as non-stationarity of market into account. For predictability analysis, the bifurcation diagrams and recurrence plots of the data have been investigated. The results of these analyses indicate existence of deterministic chaos in addition to non-stationarity property of the system which implies short-term predictability. In the next step, two artificial neural networks have been developed for forecasting the two price indices in Iran's electricity market. The models' input sets are selected regarding four aspects: the correlation properties of the available data, the critiques of Iran's electricity market, a proper convergence rate in case of sudden variations in the market price behavior, and the omission of cumulative forecasting errors. The simulation results based on experimental data from Iran electricity market are representative of good performance of the developed neural networks in coping with and forecasting of the market behavior, even in the case of severe volatility in the market price indices. (author)

  20. Study on Competitive Exporting Price-forecast of the SMART in the U.S

    International Nuclear Information System (INIS)

    Kim, In Su; Kim, Tae Ryong

    2014-01-01

    In line with this, the U.S. has a renewed interest in SMRs rather than large reactors. Nothing, however, has been implemented yet. The only SMRs under construction are in Russia: the first floating nuclear plants. For the most part, the primary candidates to be the first land-based counterparts of Russia's are the SMART (System integrated Modular Advanced Reactor) reactors. The Korean SMART has been developed and licensed for standard design. In addition, the SMART reactor may be suited to countries, which have a small grid capacity, low population density, and decentralization power system such as the U.S. Therefore, the purpose of this paper is to develop a target price for the SMR market opportunities in the U.S., competing against the CCGT (Combined Cycle Gas Turbine) which is currently a very attractive option for generating due to the shale innovation. Even though detailed cost estimates are not available, target price can be derived based on generally determining market price. This paper demonstrates the target exporting price of the SMART in the U.S. ranging from 3,091 - 4,011$/kWe depending on the scaling factor and carbon tax, assuming that discount rates are fixed. This value could be a target cost of construction, developing the U.S market whose demand of the SMART is potentially 4 units 2015 - 2035. Sensitivity analysis shows that the price goes up in proportion to the gas price, the capacity factor of the SMART, the overnight cost of CCGT, etc. More than anything else, this study reveals that carbon tax does not have much influence on the target price compared with those listed above. On the other hand, the price goes up in inverse proportion to the interest of the SMART, the capacity factor of CCGT, O and M costs of the SMART, and so on. For the price competitiveness, construction cost should first be reduced because construction cost is the largest component of LCOE as well as the effect of interest rate is the most sensitive for target price

  1. Study on Competitive Exporting Price-forecast of the SMART in the U.S

    Energy Technology Data Exchange (ETDEWEB)

    Kim, In Su; Kim, Tae Ryong [KEPCO International Nuclear Graduate School, Ulsan (Korea, Republic of)

    2014-10-15

    In line with this, the U.S. has a renewed interest in SMRs rather than large reactors. Nothing, however, has been implemented yet. The only SMRs under construction are in Russia: the first floating nuclear plants. For the most part, the primary candidates to be the first land-based counterparts of Russia's are the SMART (System integrated Modular Advanced Reactor) reactors. The Korean SMART has been developed and licensed for standard design. In addition, the SMART reactor may be suited to countries, which have a small grid capacity, low population density, and decentralization power system such as the U.S. Therefore, the purpose of this paper is to develop a target price for the SMR market opportunities in the U.S., competing against the CCGT (Combined Cycle Gas Turbine) which is currently a very attractive option for generating due to the shale innovation. Even though detailed cost estimates are not available, target price can be derived based on generally determining market price. This paper demonstrates the target exporting price of the SMART in the U.S. ranging from 3,091 - 4,011$/kWe depending on the scaling factor and carbon tax, assuming that discount rates are fixed. This value could be a target cost of construction, developing the U.S market whose demand of the SMART is potentially 4 units 2015 - 2035. Sensitivity analysis shows that the price goes up in proportion to the gas price, the capacity factor of the SMART, the overnight cost of CCGT, etc. More than anything else, this study reveals that carbon tax does not have much influence on the target price compared with those listed above. On the other hand, the price goes up in inverse proportion to the interest of the SMART, the capacity factor of CCGT, O and M costs of the SMART, and so on. For the price competitiveness, construction cost should first be reduced because construction cost is the largest component of LCOE as well as the effect of interest rate is the most sensitive for target price

  2. Price (slump) forecast : the potential impact on pipelines, producers and marketers

    International Nuclear Information System (INIS)

    Duncan, J.

    2002-01-01

    Throughout this presentation, the speaker answers three basic questions: (1) why are the prices of natural gas so high?, (2) why were the prices of natural gas so high? and (3) will prices for natural gas ever go that high again? The evolution of gas supply and demand including the Canadian supply picture is briefly reviewed. The winter of 2000 and the paradox it presented was discussed, providing a history lesson of an industry taken for granted. The cold winter of 2000 saw industry players scrambling to determine where they would get gas, and the winter of 2001 witnessed them wondering where to put this gas. The new character of the market and the players is discussed, looking at the producer, pipeline expansion projects, and the end user. Neglected investment in the sector and its consequences are dealt with in the next stage of the presentation. The synthetic supply and demand theory are examined. The author concludes the presentation by discussing the factors affecting the market today, such as storage inventory creating volatility, decrease in production and imports due to lag in time when prices are depressed, increased participation by speculators due to increased uncertainty in the stock market, recent weather questions that magnify price movements, and the environment. figs

  3. Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price

    Directory of Open Access Journals (Sweden)

    Kaijian He

    2016-04-01

    Full Text Available Recent empirical studies reveal evidence of the co-existence of heterogeneous data characteristics distinguishable by time scale in the movement crude oil prices. In this paper we propose a new multivariate Empirical Mode Decomposition (EMD-based model to take advantage of these heterogeneous characteristics of the price movement and model them in the crude oil markets. Empirical studies in benchmark crude oil markets confirm that more diverse heterogeneous data characteristics can be revealed and modeled in the projected time delayed domain. The proposed model demonstrates the superior performance compared to the benchmark models.

  4. Comparison between stochastic and machine learning methods for hydrological multi-step ahead forecasting: All forecasts are wrong!

    Science.gov (United States)

    Papacharalampous, Georgia; Tyralis, Hristos; Koutsoyiannis, Demetris

    2017-04-01

    Machine learning (ML) is considered to be a promising approach to hydrological processes forecasting. We conduct a comparison between several stochastic and ML point estimation methods by performing large-scale computational experiments based on simulations. The purpose is to provide generalized results, while the respective comparisons in the literature are usually based on case studies. The stochastic methods used include simple methods, models from the frequently used families of Autoregressive Moving Average (ARMA), Autoregressive Fractionally Integrated Moving Average (ARFIMA) and Exponential Smoothing models. The ML methods used are Random Forests (RF), Support Vector Machines (SVM) and Neural Networks (NN). The comparison refers to the multi-step ahead forecasting properties of the methods. A total of 20 methods are used, among which 9 are the ML methods. 12 simulation experiments are performed, while each of them uses 2 000 simulated time series of 310 observations. The time series are simulated using stochastic processes from the families of ARMA and ARFIMA models. Each time series is split into a fitting (first 300 observations) and a testing set (last 10 observations). The comparative assessment of the methods is based on 18 metrics, that quantify the methods' performance according to several criteria related to the accurate forecasting of the testing set, the capturing of its variation and the correlation between the testing and forecasted values. The most important outcome of this study is that there is not a uniformly better or worse method. However, there are methods that are regularly better or worse than others with respect to specific metrics. It appears that, although a general ranking of the methods is not possible, their classification based on their similar or contrasting performance in the various metrics is possible to some extent. Another important conclusion is that more sophisticated methods do not necessarily provide better forecasts

  5. THE LONG MEMORY PROPERTY OF HUNGARIAN MARKET PIG PRICES: A COMPARISON OF THREE DIFFERENT METHODS

    Directory of Open Access Journals (Sweden)

    SÁNDOR KOVÁCS

    2012-10-01

    Full Text Available The present study investigates the long memory property of market pig prices. Simply knowing that these time series have long term dependence could have strong significance when forecasting prices. The presence of long memory is crucial information in making business decisions and creating portfolios. Long memory can be measured by calculating the so-called Hurst exponent. In our article, we studied and described three different methods (Rescaled range, Detrended Fluctuation Analysis, Autoregressive Fractionally Integrated Moving Average. Data consist of four time series (piglet, young pig, sow, slaughter pig between 1991 and 2011. Before conducting the econometric analysis, all the series were seasonally adjusted using the TRAMO/SEATS method. Data preparation was followed by differencing the time series and testing their normality and stationarity. In the next step, we divided the analysed period into four parts and determined the Hurst exponent for each sub-period, using all three methods. In summary, results showed that slaughter pig prices are random; pig and piglet prices developed similarly and have long memory, while sow price changes definitely have short memory. Among the methods of pinpointing long term memory, ARFIMA was used for making the forecast. The forecasting ability of the method was compared to the traditional ARIMA model, with ARFIMA proving to be the better of the two.

  6. Logical design of a decision support system to forecast technology, prices and costs for the national communications system

    Science.gov (United States)

    Williams, K. A.; Partridge, E. C., III

    1984-09-01

    Originally envisioned as a means to integrate the many systems found throughout the government, the general mission of the NCS continues to be to ensure the survivability of communications during and subsequent to any national emergency. In order to accomplish this mission the NCS is an arrangement of heterogeneous telecommunications systems which are provided by their sponsor Federal agencies. The physical components of Federal telecommunications systems and networks include telephone and digital data switching facilities and primary common user communications centers; Special purpose local delivery message switching and exchange facilities; Government owned or leased radio systems; Technical control facilities which are under exclusive control of a government agency. This thesis describes the logical design of a proposed decision support system for use by the National Communications System in forecasting technology, prices, and costs. It is general in nature and only includes those forecasting models which are suitable for computer implementation. Because it is a logical design it can be coded and applied in many different hardware and/or software configurations.

  7. Comparison of two new short-term wind-power forecasting systems

    Energy Technology Data Exchange (ETDEWEB)

    Ramirez-Rosado, Ignacio J. [Department of Electrical Engineering, University of Zaragoza, Zaragoza (Spain); Fernandez-Jimenez, L. Alfredo [Department of Electrical Engineering, University of La Rioja, Logrono (Spain); Monteiro, Claudio; Sousa, Joao; Bessa, Ricardo [FEUP, Fac. Engenharia Univ. Porto (Portugal)]|[INESC - Instituto de Engenharia de Sistemas e Computadores do Porto, Porto (Portugal)

    2009-07-15

    This paper presents a comparison of two new advanced statistical short-term wind-power forecasting systems developed by two independent research teams. The input variables used in both systems were the same: forecasted meteorological variable values obtained from a numerical weather prediction model; and electric power-generation registers from the SCADA system of the wind farm. Both systems are described in detail and the forecasting results compared, revealing great similarities, although the proposed structures of the two systems are different. The forecast horizon for both systems is 72 h, allowing the use of the forecasted values in electric market operations, as diary and intra-diary power generation bid offers, and in wind-farm maintenance planning. (author)

  8. A comparison of alternative methods for measuring cigarette prices.

    Science.gov (United States)

    Chaloupka, Frank J; Tauras, John A; Strasser, Julia H; Willis, Gordon; Gibson, James T; Hartman, Anne M

    2015-05-01

    Government agencies, public health organisations and tobacco control researchers rely on accurate estimates of cigarette prices for a variety of purposes. Since the 1950s, the Tax Burden on Tobacco (TBOT) has served as the most widely used source of this price data despite its limitations. This paper compares the prices and collection methods of the TBOT retail-based data and the 2003 and 2006/2007 waves of the population-based Tobacco Use Supplement to the Current Population Survey (TUS-CPS). From the TUS-CPS, we constructed multiple state-level measures of cigarette prices, including weighted average prices per pack (based on average prices for single-pack purchases and average prices for carton purchases) and compared these with the weighted average price data reported in the TBOT. We also constructed several measures of tax avoidance from the TUS-CPS self-reported data. For the 2003 wave, the average TUS-CPS price was 71 cents per pack less than the average TBOT price; for the 2006/2007 wave, the difference was 47 cents. TUS-CPS and TBOT prices were also significantly different at the state level. However, these differences varied widely by state due to tax avoidance opportunities, such as cross-border purchasing. The TUS-CPS can be used to construct valid measures of cigarette prices. Unlike the TBOT, the TUS-CPS captures the effect of price-reducing marketing strategies, as well as tax avoidance practices and non-traditional types of purchasing. Thus, self-reported data like TUS-CPS appear to have advantages over TBOT in estimating the 'real' price that smokers face. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  9. Comparison on the forecast model of landfill surface

    International Nuclear Information System (INIS)

    Zhou Xiaozhi; Sang Shuxun; Cao Liwen; Ji Xiaoyan

    2010-01-01

    Using four large-scale simulated landfill equipments, indoor parallel simulation landfill experiment was carried out. By monitoring the cumulative settlement of MSW, comparable researches indicate the actual effects of 'empirical model' and 'biodegradation model' on landfill surface settlement forecast, and the optimization measures are proposed on the basis of model defects analysis. Research leaded to following results: To the short-term prediction of MSW settlement, two types of models all have satisfactory predictive validity. When performing medium and long-term prediction, 'empirical model' predicted a significant deviation from the actual, and the forecasting error of 'biodegradation model' is also gradually enlarge with the extending forecast period. For optimizing these two types of model, long-term surface settlement monitoring is fundamental method, and constantly modify the model parameters is the key according to the dynamic monitoring data. (authors)

  10. A Comparison of Various Forecasting Methods for Autocorrelated Time Series

    Directory of Open Access Journals (Sweden)

    Karin Kandananond

    2012-07-01

    Full Text Available The accuracy of forecasts significantly affects the overall performance of a whole supply chain system. Sometimes, the nature of consumer products might cause difficulties in forecasting for the future demands because of its complicated structure. In this study, two machine learning methods, artificial neural network (ANN and support vector machine (SVM, and a traditional approach, the autoregressive integrated moving average (ARIMA model, were utilized to predict the demand for consumer products. The training data used were the actual demand of six different products from a consumer product company in Thailand. Initially, each set of data was analysed using Ljung‐Box‐Q statistics to test for autocorrelation. Afterwards, each method was applied to different sets of data. The results indicated that the SVM method had a better forecast quality (in terms of MAPE than ANN and ARIMA in every category of products.

  11. Short-term electricity prices forecasting based on support vector regression and Auto-regressive integrated moving average modeling

    International Nuclear Information System (INIS)

    Che Jinxing; Wang Jianzhou

    2010-01-01

    In this paper, we present the use of different mathematical models to forecast electricity price under deregulated power. A successful prediction tool of electricity price can help both power producers and consumers plan their bidding strategies. Inspired by that the support vector regression (SVR) model, with the ε-insensitive loss function, admits of the residual within the boundary values of ε-tube, we propose a hybrid model that combines both SVR and Auto-regressive integrated moving average (ARIMA) models to take advantage of the unique strength of SVR and ARIMA models in nonlinear and linear modeling, which is called SVRARIMA. A nonlinear analysis of the time-series indicates the convenience of nonlinear modeling, the SVR is applied to capture the nonlinear patterns. ARIMA models have been successfully applied in solving the residuals regression estimation problems. The experimental results demonstrate that the model proposed outperforms the existing neural-network approaches, the traditional ARIMA models and other hybrid models based on the root mean square error and mean absolute percentage error.

  12. Comparison of two methods forecasting binding rate of plasma protein.

    Science.gov (United States)

    Hongjiu, Liu; Yanrong, Hu

    2014-01-01

    By introducing the descriptors calculated from the molecular structure, the binding rates of plasma protein (BRPP) with seventy diverse drugs are modeled by a quantitative structure-activity relationship (QSAR) technique. Two algorithms, heuristic algorithm (HA) and support vector machine (SVM), are used to establish linear and nonlinear models to forecast BRPP. Empirical analysis shows that there are good performances for HA and SVM with cross-validation correlation coefficients Rcv(2) of 0.80 and 0.83. Comparing HA with SVM, it was found that SVM has more stability and more robustness to forecast BRPP.

  13. Spatial Bayesian methods of forecasting house prices in six metropolitan areas of South Africa

    CSIR Research Space (South Africa)

    Gupta, R

    2008-06-01

    Full Text Available and Gertler, 1995), but also because changes in house prices tends to have important wealth effects on consumption (International Monetary Fund, 2000) and investment (Topel and Rosen, 1988), not allowing for heterogeneity and segmentation in the market.... One solution, often adapted, is simply to 7 The discussion in this Section relies heavily on LeSage (1999), Sichei and Gupta (2006) and Gupta (2006). Formatted: Indent: 0 cm Deleted...

  14. Computational intelligence applications to option pricing, volatility forecasting and value at risk

    CERN Document Server

    Mostafa, Fahed; Chang, Elizabeth

    2017-01-01

    The results in this book demonstrate the power of neural networks in learning complex behavior from the underlying financial time series data . The results in this book also demonstrate how neural networks can successfully be applied to volatility modeling, option pricings, and value at risk modeling. These features allow them to be applied to market risk problems to overcome classical issues associated with statistical models. .

  15. A comparison of various forecasting techniques applied to mean hourly wind speed time series

    Energy Technology Data Exchange (ETDEWEB)

    Sfetsos, A. [7 Pirsou Street, Athens (Greece)

    2000-09-01

    This paper presents a comparison of various forecasting approaches, using time series analysis, on mean hourly wind speed data. In addition to the traditional linear (ARMA) models and the commonly used feed forward and recurrent neural networks, other approaches are also examined including the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Neural Logic Networks. The developed models are evaluated for their ability to produce accurate and fast forecasts. (Author)

  16. Pricing a Protest: Forecasting the Dynamics of Civil Unrest Activity in Social Media.

    Directory of Open Access Journals (Sweden)

    Brian J Goode

    Full Text Available Online social media activity can often be a precursor to disruptive events such as protests, strikes, and "occupy" movements. We have observed that such civil unrest can galvanize supporters through social networks and help recruit activists to their cause. Understanding the dynamics of social network cascades and extrapolating their future growth will enable an analyst to detect or forecast major societal events. Existing work has primarily used structural and temporal properties of cascades to predict their future behavior. But factors like societal pressure, alignment of individual interests with broader causes, and perception of expected benefits also affect protest participation in social media. Here we develop an analysis framework using a differential game theoretic approach to characterize the cost of participating in a cascade, and demonstrate how we can combine such cost features with classical properties to forecast the future behavior of cascades. Using data from Twitter, we illustrate the effectiveness of our models on the "Brazilian Spring" and Venezuelan protests that occurred in June 2013 and November 2013, respectively. We demonstrate how our framework captures both qualitative and quantitative aspects of how these uprisings manifest through the lens of tweet volume on Twitter social media.

  17. Pricing a Protest: Forecasting the Dynamics of Civil Unrest Activity in Social Media.

    Science.gov (United States)

    Goode, Brian J; Krishnan, Siddharth; Roan, Michael; Ramakrishnan, Naren

    2015-01-01

    Online social media activity can often be a precursor to disruptive events such as protests, strikes, and "occupy" movements. We have observed that such civil unrest can galvanize supporters through social networks and help recruit activists to their cause. Understanding the dynamics of social network cascades and extrapolating their future growth will enable an analyst to detect or forecast major societal events. Existing work has primarily used structural and temporal properties of cascades to predict their future behavior. But factors like societal pressure, alignment of individual interests with broader causes, and perception of expected benefits also affect protest participation in social media. Here we develop an analysis framework using a differential game theoretic approach to characterize the cost of participating in a cascade, and demonstrate how we can combine such cost features with classical properties to forecast the future behavior of cascades. Using data from Twitter, we illustrate the effectiveness of our models on the "Brazilian Spring" and Venezuelan protests that occurred in June 2013 and November 2013, respectively. We demonstrate how our framework captures both qualitative and quantitative aspects of how these uprisings manifest through the lens of tweet volume on Twitter social media.

  18. How Should they Affect Pricing Decisions? Difficult Comparison Effect

    Directory of Open Access Journals (Sweden)

    Angela Eliza MICU

    2007-01-01

    Full Text Available In most companies, there is ongoing conflict between managers in charge of covering costs (finance and accounting and managers in charge of satisfying customers (marketing and sales. Accounting journals warn against prices that fail to cover full costs, while marketing journals argue that customer willingness-to-pay must be the sole driver of prices. The conflict between these views wastes company resources and leads to pricing decisions that are imperfect compromises. Profitable pricing involves an integration of costs and customer value. To achieve that integration, however, both need to let go of misleading ideas and form a common vision of what drives profitability.

  19. A comparison of the VAR model and the PC factor model in forecasting inflation in Montenegro

    Directory of Open Access Journals (Sweden)

    Lipovina-Božović Milena

    2013-01-01

    Full Text Available Montenegro started using the euro in 2002 and regained independence in 2006. Its main economic partners are European countries, yet inflation movements in Montenegro do not coincide with consumer price fluctuations in the eurozone. Trying to develop a useful forecasting model for Montenegrin inflation, we compare the results of a three-variable vector autoregression (VAR model, and a principle component (PC factor model starting with twelve variables. The estimation period is January 2001 to December 2012, and the control months are the first six months of 2013. The results show that in forecasting inflation, despite a high level of Montenegrin economic dependence on international developments, more reliable forecasts are achieved with the use of additional information on a larger number of factors, which includes domestic economic activity.

  20. Classical gas: Hearty prices, robust demand combine to pump breezy optimism through 2005 forecasts

    International Nuclear Information System (INIS)

    Lunan, D.

    2005-01-01

    The outlook for natural gas in 2005 is said to be a watershed year, with a lengthy list of developments that could have significant effect on the industry for many years to come. In light of continuing high demand and static supply prospects, prices will have to continue to be high in order to ensure the necessary infrastructure investments to keep gas flowing from multiple sources to the consumer. It is predicted that against the backdrop of robust prices several supply initiatives will continue to advance rapidly in 2005, such as the $7 billion Mackenzie Gas Project on which public hearings are expected to start this summer, along with regulatory clarity about the $20 billion Alaska Highway Natural Gas Pipeline Project to move North Slope gas to southern markets. Drilling of new gas wells will continue to approach or even surpass 18,000 new wells, with an increasing number of these being coal-bed methane wells. Despite high level drilling activity, supply is expected to grow only about 400 MMcf per day. Greater supply increments are expected through continued LNG terminal development, although plans for new LNG terminal development have been met with stiff resistance from local residents both in Canada and the United States. Imports of liquefied natural gas into the United States slowed dramatically in 2004 under the severe short-term downward pressure on natural gas prices, nevertheless, these imports are expected to rebound to new record highs in 2005. Capacity is expected to climb from about 2.55 Bcf per day in 2004 to as much as 6.4 Bcf per day by late 2007. At least one Canadian import facility, Anadarko's one Bcf per day Bear Head terminal on Nova Scotia's Strait of Canso, is expected to become operational by late 2007 or early 2008. 6 photos

  1. Stock price forecasting for companies listed on Tehran stock exchange using multivariate adaptive regression splines model and semi-parametric splines technique

    Science.gov (United States)

    Rounaghi, Mohammad Mahdi; Abbaszadeh, Mohammad Reza; Arashi, Mohammad

    2015-11-01

    One of the most important topics of interest to investors is stock price changes. Investors whose goals are long term are sensitive to stock price and its changes and react to them. In this regard, we used multivariate adaptive regression splines (MARS) model and semi-parametric splines technique for predicting stock price in this study. The MARS model as a nonparametric method is an adaptive method for regression and it fits for problems with high dimensions and several variables. semi-parametric splines technique was used in this study. Smoothing splines is a nonparametric regression method. In this study, we used 40 variables (30 accounting variables and 10 economic variables) for predicting stock price using the MARS model and using semi-parametric splines technique. After investigating the models, we select 4 accounting variables (book value per share, predicted earnings per share, P/E ratio and risk) as influencing variables on predicting stock price using the MARS model. After fitting the semi-parametric splines technique, only 4 accounting variables (dividends, net EPS, EPS Forecast and P/E Ratio) were selected as variables effective in forecasting stock prices.

  2. An Empirical Comparison of Default Swap Pricing Models

    NARCIS (Netherlands)

    P. Houweling (Patrick); A.C.F. Vorst (Ton)

    2002-01-01

    textabstractAbstract: In this paper we compare market prices of credit default swaps with model prices. We show that a simple reduced form model with a constant recovery rate outperforms the market practice of directly comparing bonds' credit spreads to default swap premiums. We find that the

  3. Comparison of vector autoregressive (VAR) and vector error correction models (VECM) for index of ASEAN stock price

    Science.gov (United States)

    Suharsono, Agus; Aziza, Auliya; Pramesti, Wara

    2017-12-01

    Capital markets can be an indicator of the development of a country's economy. The presence of capital markets also encourages investors to trade; therefore investors need information and knowledge of which shares are better. One way of making decisions for short-term investments is the need for modeling to forecast stock prices in the period to come. Issue of stock market-stock integration ASEAN is very important. The problem is that ASEAN does not have much time to implement one market in the economy, so it would be very interesting if there is evidence whether the capital market in the ASEAN region, especially the countries of Indonesia, Malaysia, Philippines, Singapore and Thailand deserve to be integrated or still segmented. Furthermore, it should also be known and proven What kind of integration is happening: what A capital market affects only the market Other capital, or a capital market only Influenced by other capital markets, or a Capital market as well as affecting as well Influenced by other capital markets in one ASEAN region. In this study, it will compare forecasting of Indonesian share price (IHSG) with neighboring countries (ASEAN) including developed and developing countries such as Malaysia (KLSE), Singapore (SGE), Thailand (SETI), Philippines (PSE) to find out which stock country the most superior and influential. These countries are the founders of ASEAN and share price index owners who have close relations with Indonesia in terms of trade, especially exports and imports. Stock price modeling in this research is using multivariate time series analysis that is VAR (Vector Autoregressive) and VECM (Vector Error Correction Modeling). VAR and VECM models not only predict more than one variable but also can see the interrelations between variables with each other. If the assumption of white noise is not met in the VAR modeling, then the cause can be assumed that there is an outlier. With this modeling will be able to know the pattern of relationship

  4. Comparison of Conventional and ANN Models for River Flow Forecasting

    Science.gov (United States)

    Jain, A.; Ganti, R.

    2011-12-01

    Hydrological models are useful in many water resources applications such as flood control, irrigation and drainage, hydro power generation, water supply, erosion and sediment control, etc. Estimates of runoff are needed in many water resources planning, design development, operation and maintenance activities. River flow is generally estimated using time series or rainfall-runoff models. Recently, soft artificial intelligence tools such as Artificial Neural Networks (ANNs) have become popular for research purposes but have not been extensively adopted in operational hydrological forecasts. There is a strong need to develop ANN models based on real catchment data and compare them with the conventional models. In this paper, a comparative study has been carried out for river flow forecasting using the conventional and ANN models. Among the conventional models, multiple linear, and non linear regression, and time series models of auto regressive (AR) type have been developed. Feed forward neural network model structure trained using the back propagation algorithm, a gradient search method, was adopted. The daily river flow data derived from Godavari Basin @ Polavaram, Andhra Pradesh, India have been employed to develop all the models included here. Two inputs, flows at two past time steps, (Q(t-1) and Q(t-2)) were selected using partial auto correlation analysis for forecasting flow at time t, Q(t). A wide range of error statistics have been used to evaluate the performance of all the models developed in this study. It has been found that the regression and AR models performed comparably, and the ANN model performed the best amongst all the models investigated in this study. It is concluded that ANN model should be adopted in real catchments for hydrological modeling and forecasting.

  5. Apartment price determinants : A comparison between Sweden and Germany

    OpenAIRE

    Anop, Sviatlana

    2015-01-01

    Similar development of economic fundamentals in Germany over the last two decades did not lead to the same dramatic house price increases as it is in Sweden. What can explain this house price stability over a long period? This thesis attempts to find the answer this question. The first paper in this thesis contains an extended literature review on the studies focused on the factors affecting house prices in the short and in the long run. Existing literature adopts a broad variation of approac...

  6. Pricing of alcohol in Canada: A comparison of provincial policies and harm-reduction opportunities.

    Science.gov (United States)

    Giesbrecht, Norman; Wettlaufer, Ashley; Thomas, Gerald; Stockwell, Tim; Thompson, Kara; April, Nicole; Asbridge, Mark; Cukier, Samantha; Mann, Robert; McAllister, Janet; Murie, Andrew; Pauley, Chris; Plamondon, Laurie; Vallance, Kate

    2016-05-01

    Alcohol pricing is an effective prevention policy. This paper compares the 10 Canadian provinces on three research-based alcohol pricing policies-minimum pricing, pricing by alcohol content and maintaining prices relative to inflation. The selection of these three policies was based on systematic reviews and seminal research papers. Provincial data for 2012 were obtained from Statistics Canada and relevant provincial ministries, subsequently sent to provincial authorities for verification, and then scored by team members. All provinces, except for Alberta, have minimum prices for at least one beverage type sold in off-premise outlets. All provinces, except for British Columbia and Quebec, have separate (and higher) minimum pricing for on-premise establishments. Regarding pricing on alcohol content, western and central provinces typically scored higher than provinces in Eastern Canada. Generally, minimum prices were lower than the recommended $1.50 per standard drink for off-premise outlets and $3.00 per standard drink in on-premise venues. Seven of 10 provinces scored 60% or higher compared to the ideal on indexing prices to inflation. Prices for a representative basket of alcohol products in Ontario and Quebec have lagged significantly behind inflation since 2006. While examples of evidence-based alcohol pricing policies can be found in every jurisdiction in Canada, significant inter-provincial variation leaves substantial unrealised potential for further reducing alcohol-related harm and costs. This comparative assessment of alcohol price policies provides clear indications of how individual provinces could adjust their pricing policies and practices to improve public health and safety. [Giesbrecht N, Wettlaufer A, Thomas G, Stockwell T, Thompson K, April N, Asbridge M, Cukier S, Mann R, McAllister J, Murie A, Pauley C, Plamondon L, Vallance K. Pricing of alcohol in Canada: A comparison of provincial policies and harm-reduction opportunities. Drug Alcohol Rev

  7. Study on Comparison of Bidding and Pricing Behavior Distinction between Estimate Methods

    Science.gov (United States)

    Morimoto, Emi; Namerikawa, Susumu

    The most characteristic trend on bidding and pricing behavior distinction in recent years is the increasing number of bidders just above the criteria for low-price bidding investigations. The contractor's markup is the difference between the bidding price and the execution price. Therefore, the contractor's markup is the difference between criteria for low-price bidding investigations price and the execution price in the public works bid in Japan. Virtually, bidder's strategies and behavior have been controlled by public engineer's budgets. Estimation and bid are inseparably linked in the Japanese public works procurement system. The trial of the unit price-type estimation method begins in 2004. On another front, accumulated estimation method is one of the general methods in public works. So, there are two types of standard estimation methods in Japan. In this study, we did a statistical analysis on the bid information of civil engineering works for the Ministry of Land, Infrastructure, and Transportation in 2008. It presents several issues that bidding and pricing behavior is related to an estimation method (several estimation methods) for public works bid in Japan. The two types of standard estimation methods produce different results that number of bidders (decide on bid-no bid strategy) and distribution of bid price (decide on mark-up strategy).The comparison on the distribution of bid prices showed that the percentage of the bid concentrated on the criteria for low-price bidding investigations have had a tendency to get higher in the large-sized public works by the unit price-type estimation method, comparing with the accumulated estimation method. On one hand, the number of bidders who bids for public works estimated unit-price tends to increase significantly Public works estimated unit-price is likely to have been one of the factors for the construction companies to decide if they participate in the biddings.

  8. A comparison of generic drug prices in seven European countries: a methodological analysis.

    Science.gov (United States)

    Wouters, Olivier J; Kanavos, Panos G

    2017-03-31

    Policymakers and researchers frequently compare the prices of medicines between countries. Such comparisons often serve as barometers of how pricing and reimbursement policies are performing. The aim of this study was to examine methodological challenges to comparing generic drug prices. We calculated all commonly used price indices based on 2013 IMS Health data on sales of 3156 generic drugs in seven European countries. There were large differences in generic drug prices between countries. However, the results varied depending on the choice of index, base country, unit of volume, method of currency conversion, and therapeutic category. The results also differed depending on whether one looked at the prices charged by manufacturers or those charged by pharmacists. Price indices are a useful statistical approach for comparing drug prices across countries, but researchers and policymakers should interpret price indices with caution given their limitations. Price-index results are highly sensitive to the choice of method and sample. More research is needed to determine the drivers of price differences between countries. The data suggest that some governments should aim to reduce distribution costs for generic drugs.

  9. Comparison of historically simulated VaR: Evidence from oil prices

    International Nuclear Information System (INIS)

    Costello, Alexandra; Asem, Ebenezer; Gardner, Eldon

    2008-01-01

    Cabedo and Moya [Cabedo, J.D., Moya, I., 2003. Estimating oil price 'Value at Risk' using the historical simulation approach. Energy Economics 25, 239-253] find that ARMA with historical simulation delivers VaR forecasts that are superior to those from GARCH. We compare the ARMA with historical simulation to the semi-parametric GARCH model proposed by Barone-Adesi et al. [Barone-Adesi, G., Giannopoulos, K., Vosper, L., 1999. VaR without correlations for portfolios of derivative securities. Journal of Futures Markets 19 (5), 583-602]. The results suggest that the semi-parametric GARCH model generates VaR forecasts that are superior to the VaR forecasts from the ARMA with historical simulation. This is due to the fact that GARCH captures volatility clustering. Our findings suggest that Cabedo and Moya's conclusion is mainly driven by the normal distributional assumption imposed on the future risk structure in the GARCH model. (author)

  10. What Do Experts Know About Forecasting Journal Quality? A Comparison with ISI Research Impact in Finance?

    NARCIS (Netherlands)

    C-L. Chang (Chia-Lin); M.J. McAleer (Michael)

    2013-01-01

    textabstractExperts possess knowledge and information that are not publicly available. The paper is concerned with forecasting academic journal quality and research impact using a survey of international experts from a national project on ranking academic finance journals in Taiwan. A comparison is

  11. What Do Experts Know About Forecasting Journal Quality? A Comparison with ISI Research Impact in Finance

    NARCIS (Netherlands)

    C-L. Chang (Chia-Lin); M.J. McAleer (Michael)

    2013-01-01

    textabstractExperts possess knowledge and information that are not publicly available. The paper is concerned with forecasting academic journal quality and research impact using a survey of international experts from a national project on ranking academic finance journals in Taiwan. A comparison is

  12. Assumptions for the Annual Energy Outlook 1992

    International Nuclear Information System (INIS)

    1992-01-01

    This report serves a auxiliary document to the Energy Information Administration (EIA) publication Annual Energy Outlook 1992 (AEO) (DOE/EIA-0383(92)), released in January 1992. The AEO forecasts were developed for five alternative cases and consist of energy supply, consumption, and price projections by major fuel and end-use sector, which are published at a national level of aggregation. The purpose of this report is to present important quantitative assumptions, including world oil prices and macroeconomic growth, underlying the AEO forecasts. The report has been prepared in response to external requests, as well as analyst requirements for background information on the AEO and studies based on the AEO forecasts

  13. Price

    International Nuclear Information System (INIS)

    Anon.

    1991-01-01

    The price terms in wheeling contracts very substantially, reflecting the differing conditions affecting the parties contracting for the service. These terms differ in the manner in which rates are calculated, the formulas used, and the philosophy underlying the accord. For example, and EEI study found that firm wheeling rates ranged from 20 cents to $1.612 per kilowatt per month. Nonfirm rates ranged from .15 mills to 5.25 mills per kilowatt-hour. The focus in this chapter is on cost-based rates, reflecting the fact that the vast majority of existing contracts are based on rate designs reflecting embedded costs. This situation may change in the future, but, for now, this fact can't be ignored

  14. Implementing US GDP in Chained Prices for Cross-country GDP Growth and Sectoral Comparisons: Application to Selected ASEAN Countries

    OpenAIRE

    Dumagan, Jesus C.

    2008-01-01

    GDP in constant prices of ASEAN countries suffers from substitution bias by ignoring relative price changes and makes GDP growth and shares dependent on the base year. These analytical deficiencies led the US since the mid-1990s to convert GDP from constant to chained prices. Thus, cross-country comparisons in constant prices are analytically shaky even with the same base year. Therefore, this paper implements US GDP in chained prices in Indonesia, Malaysia, Philippines, and Thailand to allev...

  15. Vermont stumpage price trends revisited: with comparisons to New Hampshire

    Science.gov (United States)

    Paul E. Sendak; Thom J. McEvoy

    2013-01-01

    Our objective was to describe the trends of Vermont stumpage prices for major sawtimber species and products over the approximately last quarter century and propose a likely explanation of changes that we observed. Annual percentage rates of change (APR) were estimated for 10 forest products in Vermont from the third quarter of 1981 (1981Q3) to the first quarter of...

  16. An ill-posed problem for the Black-Scholes equation for a profitable forecast of prices of stock options on real market data

    Science.gov (United States)

    Klibanov, Michael V.; Kuzhuget, Andrey V.; Golubnichiy, Kirill V.

    2016-01-01

    A new empirical mathematical model for the Black-Scholes equation is proposed to forecast option prices. This model includes new interval for the price of the underlying stock, new initial and new boundary conditions. Conventional notions of maturity time and strike prices are not used. The Black-Scholes equation is solved as a parabolic equation with the reversed time, which is an ill-posed problem. Thus, a regularization method is used to solve it. To verify the validity of our model, real market data for 368 randomly selected liquid options are used. A new trading strategy is proposed. Our results indicates that our method is profitable on those options. Furthermore, it is shown that the performance of two simple extrapolation-based techniques is much worse. We conjecture that our method might lead to significant profits of those financial insitutions which trade large amounts of options. We caution, however, that further studies are necessary to verify this conjecture.

  17. A comparison of pay-as-bid and marginal pricing in electricity markets

    Science.gov (United States)

    Ren, Yongjun

    This thesis investigates the behaviour of electricity markets under marginal and pay-as-bid pricing. Marginal pricing is believed to yield the maximum social welfare and is currently implemented by most electricity markets. However, in view of recent electricity market failures, pay-as-bid has been extensively discussed as a possible alternative to marginal pricing. In this research, marginal and pay-as-bid pricing have been analyzed in electricity markets with both perfect and imperfect competition. The perfect competition case is studied under both exact and uncertain system marginal cost prediction. The comparison of the two pricing methods is conducted through two steps: (i) identify the best offer strategy of the generating companies (gencos); (ii) analyze the market performance under these optimum genco strategies. The analysis results together with numerical simulations show that pay-as-bid and marginal pricing are equivalent in a perfect market with exact system marginal cost prediction. In perfect markets with uncertain demand prediction, the two pricing methods are also equivalent but in an expected value sense. If we compare from the perspective of second order statistics, all market performance measures exhibit much lower values under pay-as-bid than under marginal pricing. The risk of deviating from the mean is therefore much higher under marginal pricing than under pay-as-bid. In an imperfect competition market with exact demand prediction, the research shows that pay-as-bid pricing yields lower consumer payments and lower genco profits. This research provides quantitative evidence that challenges some common claims about pay-as-bid pricing. One is that under pay-as-bid, participants would soon learn how to offer so as to obtain the same or higher profits than what they would have obtained under marginal pricing. This research however shows that, under pay-as-bid, participants can at best earn the same profit or expected profit as under marginal

  18. Petroleum price

    International Nuclear Information System (INIS)

    Maurice, J.

    2001-01-01

    The oil market is the most volatile of all markets, with the exception of the Nasdaq. It is also the biggest commodity market in the world. Therefore one cannot avoid forecasting oil prices, nor can one expect to avoid the forecasting errors that have been made in the past. In his report, Joel Maurice draws a distinction between the short term and the medium-long term in analysing the outlook for oil prices. (author)

  19. Comparison of two uranium-market forecasting models

    International Nuclear Information System (INIS)

    Bleistein, S.; Recek, J.

    1983-01-01

    The techniques and methodologies, similarities and differences, and the results of two uranium market computer models - the Uranium Supply Analysis System and the EUREKA model - are surveyed. These models can be of use to electric utilities in developing procurement strategies or planning new reactor requirements. The models are designed to simulate actual market performance of the domestic uranium industry under varying user-specified assumptions. These models provide output in the form of projections of variables of interest, such as investment in exploration and new production capacity, additions to reserves and resources, and adjustments in inventories. Comparison between the models is demonstrative of how output can vary even with use of the same input data. Utilities may profit by the comparison with respect to the task of selecting models on the basis of obtaining the most-useful solution for a given problem. 18 figures

  20. Canary tomato export prices: comparison and relationships between daily seasonal patterns

    Directory of Open Access Journals (Sweden)

    G. Martin-Rodriguez

    2013-10-01

    Full Text Available Statistical procedures are proposed to describe, compare and forecast the behaviour of seasonal variations in two daily price series of Canary tomato exported to German and British markets, respectively, over the last decade. These seasonal patterns are pseudo-periodic as the length of the seasonal period changes frequently in dependence of market conditions. Seasonal effect at a day in the harvesting period is defined as a spline function of the proportion of the length of such a period elapsed up to such a day. Then, seasonal patterns for the two series are compared in terms of the area between the corresponding spline functions. The ability of these models to capture the dynamic process of change in the seasonal pattern is useful to forecasting purpose. Furthermore, an analytical tool is also proposed to obtain forecasts of the seasonal pattern in one of these two series from the forecasts of the seasonal pattern in the other one. These procedures are useful for farmers in developing strategies related to the seasonal distribution of tomato production exported to each market.

  1. Military and Private Sector Commodity Outlets: A Retail Price Comparison

    Science.gov (United States)

    1985-02-01

    89 6. Del Monte Bananas, 1 lb. .34 .49 7. Sunkist Oranges , 1 lb. .37 .56 8. Minute Maid Frozen Lemon Juice, 6 fl. oz. .25 .41 9. Red Delicious Apples...continued Prices Iter Commissary Safeway 47. Marlboro Cigarettes, Carton 5.28 7.99 48. Carnation Evaporated Milk, Canned, 13 fl. oz. .46 .47 49. Gerber...1 lb. .90 1.29 80. Jimmy Dean Pork Sausage, 16 oz. 1.53 2.49 81. Minute Maid Frozen Orange Juice, 12 f!. oz. .97 .99 TOTAL $114.70 $152.23 1. Sale

  2. Forecast of oil price and consumption in the short term under three scenarios: Parabolic, linear and chaotic behaviour

    International Nuclear Information System (INIS)

    Gori, F.; Ludovisi, D.; Cerritelli, P.F.

    2007-01-01

    The paper examines the evolution of price and consumption of oil in the last decades to construct a relationship between them. Then the work considers three possible scenarios of oil price: parabolic, linear and chaotic behaviour, to predict the evolution of price and consumption of oil up to December 2003

  3. Study the Effect of Value-Added of Services Sector on Forecasting of Electricity Demand in Services Sector due to Price Reform

    Directory of Open Access Journals (Sweden)

    Sayed Mahdi Mostafavi

    2016-07-01

    Full Text Available Electrical energy is as one of the important effective factors on economic growth and development. In recent decades, numerous studies in different countries to estimate and forecast electricity demand in different parts of the economy have been made. In this paper, using the method ARDL, estimation and forecasting of electricity demand in the services sector of Iran are determined for the time period from 1983 to 2012. Estimated equations show that the added value of the services sector and a significant positive impact on the demand for electricity in this sector. The price elasticity for services sector is smaller than 1 due to low electricity prices and subsidized electricity. Hence, electricity prices have little impact on the demand for electricity. The results of the estimate represents a long-term relationship between the variables in the services sector. In this paper, based on amendments to the law on subsidies and estimated values, anticipated electricity demand until the end of the fifth development plan was carried out. The results indicate an increase in power consumption in the services sector.

  4. 16 CFR 233.2 - Retail price comparisons; comparable value comparisons.

    Science.gov (United States)

    2010-01-01

    ... business). This may be done either on a temporary or a permanent basis, but in either case the advertised...) Another commonly used form of bargain advertising is to offer goods at prices lower than those being... be reasonably certain that the higher price he advertises does not appreciably exceed the price at...

  5. Toward crustacean without chemicals: a descriptive analysis of consumer response using price comparisons.

    Science.gov (United States)

    Okpala, Charles Odilichukwu R; Bono, Gioacchino; Pipitone, Vito; Vitale, Sergio; Cannizzaro, Leonardo

    2016-01-01

    To date, there seems to be limited-to-zero emphasis about how consumers perceive crustacean products subject to either chemical and or non-chemical preservative treatments. In addition, studies that investigated price comparisons of crustacean products subject to either chemical or chemical-free preservative methods seem unreported. This study focused on providing some foundational knowledge about how consumers perceive traditionally harvested crustaceans that are either chemical-treated and or free of chemicals, incorporating price comparisons using a descriptive approach. The study design employed a questionnaire approach via interview using a computer-assisted telephone system and sampled 1,540 participants across five key locations in Italy. To actualize consumer sensitivity, 'price' was the focus given its crucial role as a consumption barrier. Prior to this, variables such as demographic characteristics of participants, frequency of purchasing, quality attributes/factors that limit the consumption of crustaceans were equally considered. By price comparisons, consumers are likely to favor chemical-free (modified atmosphere packaging) crustacean products amid a price increase of up to 15%. But, a further price increase such as by 25% could markedly damage consumers' feelings, which might lead to a considerable number opting out in favor of either chemical-treated or other seafood products. Comparing locations, the studied variables showed no statistical differences ( p >0.05). On the contrary, the response weightings fluctuated across the studied categories. Both response weightings and coefficient of variation helped reveal more about how responses deviated per variable categories. This study has revealed some foundational knowledge about how consumers perceive traditionally harvested crustaceans that were either chemical-treated or subject to chemical-free preservative up to price sensitivity using Italy as a reference case, which is applicable to other parts

  6. Comparison of historically simulated VaR: Evidence from oil prices

    Energy Technology Data Exchange (ETDEWEB)

    Costello, Alexandra [Seminole Canada Energy, Calgary, AB (Canada); Asem, Ebenezer; Gardner, Eldon [Faculty of Management, University of Lethbridge, Lethbridge, AB (Canada)

    2008-09-15

    Cabedo and Moya [Cabedo, J.D., Moya, I., 2003. Estimating oil price 'Value at Risk' using the historical simulation approach. Energy Economics 25, 239-253] find that ARMA with historical simulation delivers VaR forecasts that are superior to those from GARCH. We compare the ARMA with historical simulation to the semi-parametric GARCH model proposed by Barone-Adesi et al. [Barone-Adesi, G., Giannopoulos, K., Vosper, L., 1999. VaR without correlations for portfolios of derivative securities. Journal of Futures Markets 19 (5), 583-602]. The results suggest that the semi-parametric GARCH model generates VaR forecasts that are superior to the VaR forecasts from the ARMA with historical simulation. This is due to the fact that GARCH captures volatility clustering. Our findings suggest that Cabedo and Moya's conclusion is mainly driven by the normal distributional assumption imposed on the future risk structure in the GARCH model. (author)

  7. Daily Peak Load Forecasting of Next Day using Weather Distribution and Comparison Value of Each Nearby Date Data

    Science.gov (United States)

    Ito, Shigenobu; Yukita, Kazuto; Goto, Yasuyuki; Ichiyanagi, Katsuhiro; Nakano, Hiroyuki

    By the development of industry, in recent years; dependence to electric energy is growing year by year. Therefore, reliable electric power supply is in need. However, to stock a huge amount of electric energy is very difficult. Also, there is a necessity to keep balance between the demand and supply, which changes hour after hour. Consequently, to supply the high quality and highly dependable electric power supply, economically, and with high efficiency, there is a need to forecast the movement of the electric power demand carefully in advance. And using that forecast as the source, supply and demand management plan should be made. Thus load forecasting is said to be an important job among demand investment of electric power companies. So far, forecasting method using Fuzzy logic, Neural Net Work, Regression model has been suggested for the development of forecasting accuracy. Those forecasting accuracy is in a high level. But to invest electric power in higher accuracy more economically, a new forecasting method with higher accuracy is needed. In this paper, to develop the forecasting accuracy of the former methods, the daily peak load forecasting method using the weather distribution of highest and lowest temperatures, and comparison value of each nearby date data is suggested.

  8. Toward crustacean without chemicals: a descriptive analysis of consumer response using price comparisons

    Directory of Open Access Journals (Sweden)

    Charles Odilichukwu R. Okpala

    2016-10-01

    Full Text Available Background: To date, there seems to be limited-to-zero emphasis about how consumers perceive crustacean products subject to either chemical and or non-chemical preservative treatments. In addition, studies that investigated price comparisons of crustacean products subject to either chemical or chemical-free preservative methods seem unreported. Objective: This study focused on providing some foundational knowledge about how consumers perceive traditionally harvested crustaceans that are either chemical-treated and or free of chemicals, incorporating price comparisons using a descriptive approach. Design: The study design employed a questionnaire approach via interview using a computer-assisted telephone system and sampled 1,540 participants across five key locations in Italy. To actualize consumer sensitivity, ‘price’ was the focus given its crucial role as a consumption barrier. Prior to this, variables such as demographic characteristics of participants, frequency of purchasing, quality attributes/factors that limit the consumption of crustaceans were equally considered. Results: By price comparisons, consumers are likely to favor chemical-free (modified atmosphere packaging crustacean products amid a price increase of up to 15%. But, a further price increase such as by 25% could markedly damage consumers’ feelings, which might lead to a considerable number opting out in favor of either chemical-treated or other seafood products. Comparing locations, the studied variables showed no statistical differences (p>0.05. On the contrary, the response weightings fluctuated across the studied categories. Both response weightings and coefficient of variation helped reveal more about how responses deviated per variable categories. Conclusions: This study has revealed some foundational knowledge about how consumers perceive traditionally harvested crustaceans that were either chemical-treated or subject to chemical-free preservative up to price

  9. World market of crude oil - review of possible scenarios of forecasting for the crude oil price movement

    International Nuclear Information System (INIS)

    Janevski, Risto

    2003-01-01

    Throughout most of 2002, crude oil prices were solidly within the range preferred by producers in the Organization of Petroleum Exporting Countries (OPEC), $22 to $28 per barrel for the OPEC 'basket price' (Fig. 1). OPEC producers have been demonstrating disciplined adherence to announced cutbacks in production. Early in 2003, a dramatic upward turn in crude oil prices was brought about by a combination of two factors. First, a general strike against the Chavez regime resulted in a sudden drop in Venezuela's oil exports. Although other OPEC producers agreed to increase production to make up for the lost Venezuelan output, the obvious strain on worldwide spare capacity kept prices high. Second, price volatility was exacerbated by fears of war in Iraq. (Original)

  10. Comparison of extended mean-reversion and time series models for electricity spot price simulation considering negative prices

    International Nuclear Information System (INIS)

    Keles, Dogan; Genoese, Massimo; Möst, Dominik; Fichtner, Wolf

    2012-01-01

    This paper evaluates different financial price and time series models, such as mean reversion, autoregressive moving average (ARMA), integrated ARMA (ARIMA) and general autoregressive conditional heteroscedasticity (GARCH) process, usually applied for electricity price simulations. However, as these models are developed to describe the stochastic behaviour of electricity prices, they are extended by a separate data treatment for the deterministic components (trend, daily, weekly and annual cycles) of electricity spot prices. Furthermore price jumps are considered and implemented within a regime-switching model. Since 2008 market design allows for negative prices at the European Energy Exchange, which also occurred for several hours in the last years. Up to now, only a few financial and time series approaches exist, which are able to capture negative prices. This paper presents a new approach incorporating negative prices. The evaluation of the different approaches presented points out that the mean reversion and the ARMA models deliver the lowest mean root square error between simulated and historical electricity spot prices gained from the European Energy Exchange. These models posses also lower mean average errors than GARCH models. Hence, they are more suitable to simulate well-fitting price paths. Furthermore it is shown that the daily structure of historical price curves is better captured applying ARMA or ARIMA processes instead of mean-reversion or GARCH models. Another important outcome of the paper is that the regime-switching approach and the consideration of negative prices via the new proposed approach lead to a significant improvement of the electricity price simulation. - Highlights: ► Considering negative prices improves the results of time-series and financial models for electricity prices. ► Regime-switching approach captures the jumps and base prices quite well. ► Removing and separate modelling of deterministic annual, weekly and daily

  11. Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico

    OpenAIRE

    Johansson, Michael A.; Reich, Nicholas G.; Hota, Aditi; Brownstein, John S.; Santillana, Mauricio

    2016-01-01

    Dengue viruses, which infect millions of people per year worldwide, cause large epidemics that strain healthcare systems. Despite diverse efforts to develop forecasting tools including autoregressive time series, climate-driven statistical, and mechanistic biological models, little work has been done to understand the contribution of different components to improved prediction. We developed a framework to assess and compare dengue forecasts produced from different types of models and evaluate...

  12. Contrail Cirrus Forecasts for the ML-CIRRUS Experiment and Some Comparison Results

    Science.gov (United States)

    Schumann, Ulrich; Graf, Kaspar; Bugliaro, Luca; Dörnbrack, Andreas; Giez, Andreas; Jurkat, Tina; Kaufmann, Stefan; Krämer, Martina; Minikin, Andreas; Schäfler, Andreas; Voigt, Christiane; Wirth, Martin; Zahn, Andreas; Ziereis, Helmut

    2015-04-01

    rerun with improved ECMWF-NWP data (at one-hour time resolution). The model results are included in the HALO mission data bank, and the results are available for comparison to in-situ data. The data are useful for identifying aircraft and other sources for measured air properties. The joint analysis of observations and model result has basically just started. Preliminary results from comparisons with lidar-measured extinction profiles, in-situ measured humidity, nitrogen oxides, and aerosol and ice particle concentrations, and with meteorological observations (wind, temperature etc.) illustrate the expected gain in insight. The contrail forecasts have been checked by comparison to available data including satellite data and HALO observations. During the campaign, it became obvious that predicted contrail cirrus cover compared qualitatively mostly well with what was found when HALO reached predicted cirrus regions. From the analysis of the measured data, some examples of significant correlation between model results and observations have been found. However, the quantitative agreement is not uniform. As expected, nature is far more variable than a model can predict. The observed optical properties of cirrus and contrails vary far more in time and space than predicted. Local values were often far higher or lower than mean values. A one-to-one correlation between local observations and model results is not to be expected. This inhomogeneity may have consequences for the climate impact of aviation induced cloud changes.

  13. Comparison of ensemble post-processing approaches, based on empirical and dynamical error modelisation of rainfall-runoff model forecasts

    Science.gov (United States)

    Chardon, J.; Mathevet, T.; Le Lay, M.; Gailhard, J.

    2012-04-01

    In the context of a national energy company (EDF : Electricité de France), hydro-meteorological forecasts are necessary to ensure safety and security of installations, meet environmental standards and improve water ressources management and decision making. Hydrological ensemble forecasts allow a better representation of meteorological and hydrological forecasts uncertainties and improve human expertise of hydrological forecasts, which is essential to synthesize available informations, coming from different meteorological and hydrological models and human experience. An operational hydrological ensemble forecasting chain has been developed at EDF since 2008 and is being used since 2010 on more than 30 watersheds in France. This ensemble forecasting chain is characterized ensemble pre-processing (rainfall and temperature) and post-processing (streamflow), where a large human expertise is solicited. The aim of this paper is to compare 2 hydrological ensemble post-processing methods developed at EDF in order improve ensemble forecasts reliability (similar to Monatanari &Brath, 2004; Schaefli et al., 2007). The aim of the post-processing methods is to dress hydrological ensemble forecasts with hydrological model uncertainties, based on perfect forecasts. The first method (called empirical approach) is based on a statistical modelisation of empirical error of perfect forecasts, by streamflow sub-samples of quantile class and lead-time. The second method (called dynamical approach) is based on streamflow sub-samples of quantile class and streamflow variation, and lead-time. On a set of 20 watersheds used for operational forecasts, results show that both approaches are necessary to ensure a good post-processing of hydrological ensemble, allowing a good improvement of reliability, skill and sharpness of ensemble forecasts. The comparison of the empirical and dynamical approaches shows the limits of the empirical approach which is not able to take into account hydrological

  14. Accounting for fuel price risk when comparing renewable togas-fired generation: the role of forward natural gas prices

    Energy Technology Data Exchange (ETDEWEB)

    Bolinger, Mark; Wiser, Ryan; Golove, William

    2004-07-17

    Unlike natural gas-fired generation, renewable generation (e.g., from wind, solar, and geothermal power) is largely immune to fuel price risk. If ratepayers are rational and value long-term price stability, then--contrary to common practice--any comparison of the levelized cost of renewable to gas-fired generation should be based on a hedged gas price input, rather than an uncertain gas price forecast. This paper compares natural gas prices that can be locked in through futures, swaps, and physical supply contracts to contemporaneous long-term forecasts of spot gas prices. We find that from 2000-2003, forward gas prices for terms of 2-10 years have been considerably higher than most contemporaneous long-term gas price forecasts. This difference is striking, and implies that comparisons between renewable and gas-fired generation based on these forecasts over this period have arguably yielded results that are biased in favor of gas-fired generation.

  15. Accounting for fuel price risk when comparing renewable to gas-fired generation: the role of forward natural gas prices

    International Nuclear Information System (INIS)

    Bolinger, Mark; Wiser, Ryan; Golove, William

    2006-01-01

    Unlike natural gas-fired generation, renewable generation (e.g., from wind, solar, and geothermal power) is largely immune to fuel price risk. If ratepayers are rational and value long-term price stability, then-contrary to common practice-any comparison of the levelized cost of renewable to gas-fired generation should be based on a hedged gas price input, rather than an uncertain gas price forecast. This paper compares natural gas prices that can be locked in through futures, swaps, and physical supply contracts to contemporaneous long-term forecasts of spot gas prices. We find that from 2000 to 2003, forward gas prices for terms of 2-10 years have been considerably higher than most contemporaneous long-term gas price forecasts. This difference is striking, and implies that comparisons between renewable and gas-fired generation based on these forecasts over this period have arguably yielded results that are biased in favor of gas-fired generation

  16. Forecasting Changes in Stock Prices on the Basis of Patterns Identified with the Use of Data Classification Methods

    Directory of Open Access Journals (Sweden)

    Szanduła Jacek

    2014-06-01

    Full Text Available The paper develops the concept of harnessing data classification methods to recognize patterns in stock prices. The author defines a formation as a pattern vector describing the financial instrument. Elements of such a vector can be related to the stock price as well as sales volume and other characteristics of the financial instrument. The study uses data concerning selected companies listed on the stock exchange in New York. It takes into account a number of variables that describe the behavior of prices and volume, both in the short and long term. Partitioning around medoids method has been used for data classification (for pattern recognition. An evaluation of the possibility of using certain formations for practical purposes has also been presented.

  17. Forecast Accuracy Uncertainty and Momentum

    OpenAIRE

    Bing Han; Dong Hong; Mitch Warachka

    2009-01-01

    We demonstrate that stock price momentum and earnings momentum can result from uncertainty surrounding the accuracy of cash flow forecasts. Our model has multiple information sources issuing cash flow forecasts for a stock. The investor combines these forecasts into an aggregate cash flow estimate that has minimal mean-squared forecast error. This aggregate estimate weights each cash flow forecast by the estimated accuracy of its issuer, which is obtained from their past forecast errors. Mome...

  18. Annual energy outlook 1994: With projections to 2010

    Energy Technology Data Exchange (ETDEWEB)

    1994-01-01

    The Annual Energy Outlook 1994 (AEO94) presents the midterm energy forecasts of the Energy Information Administration (EIA). This year`s report presents projects and analyses of energy supply, demand, and prices through 2010, based for the first time on results from the National Energy Modeling System (NEMS). NEMS is the latest in a series of computer-based energy modeling systems used over the past 2 decades by EIA and its predecessor organization, the Federal Energy Administration, to analyze and forecast energy consumption and supply in the midterm period (about 20 years). Quarterly forecasts of energy supply and demand for 1994 and 1995 are published in the Short-Term Energy Outlook (February 1994). Forecast tables for 2000, 2005, and 2010 for each of the five scenarios examined in the AEO94 are provided in Appendices A through E. The five scenarios include a reference case and four additional cases that assume higher and lower economic growth and higher and lower world oil prices. Appendix F provides detailed comparisons of the AEO94 forecasts with those of other organizations. Appendix G briefly described the NEMS and the major AEO94 forecast assumptions. Appendix H summarizes the key results for the five scenarios.

  19. Projection of coal prices in international turnover in comparison to other primary energy sources; Przewidywane ceny wegla w handlu miedzynarodowym w porownaniu z innymi nosnikami energii pierwotnej

    Energy Technology Data Exchange (ETDEWEB)

    Gawlik, L.; Grudzinski, Z. [Polish Academy of Sciences, Krakow (Poland). Mineral and Energy Economy Institute

    2004-07-01

    The paper looks at trends in both steam coal and coking coal prices up to the year 2000 and then compares the trends of coal with crude oil and natural gas for the years 1987-2001. It presents forecasts for fossil fuel prices up to the year 2015. Despite temporary fluctuation, prices of coal are predicted to remain relatively stable as far as 2030, with a slight tendency to grow. 11 refs., 6 figs., 1 tab.

  20. Analyzing the Stability of Price Response Functions: Measuring the Influence of Different Parameters in a Monte Carlo Comparison

    Science.gov (United States)

    Brusch, Michael; Baier, Daniel

    The usage and the estimation of price response function is very important for strategic marketing decisions. Typically price response functions with an empirical basis are used. However, such price response functions are subject to a lot of disturbing influence factors, e.g., the assumed profit maximum price and the assumed corresponding quantity of sales. In such cases, the question how stable the found price response function is was not answered sufficiently up to now. In this paper, the question will be pursued how much (and what kind of) errors in market research are pardonable for a stable price response function. For the comparisons, a factorial design with synthetically generated and disturbed data is used.

  1. Comparison between GSTAR and GSTAR-Kalman Filter models on inflation rate forecasting in East Java

    Science.gov (United States)

    Rahma Prillantika, Jessica; Apriliani, Erna; Wahyuningsih, Nuri

    2018-03-01

    Up to now, we often find data which have correlation between time and location. This data also known as spatial data. Inflation rate is one type of spatial data because it is not only related to the events of the previous time, but also has relevance to the other location or elsewhere. In this research, we do comparison between GSTAR model and GSTAR-Kalman Filter to get prediction which have small error rate. Kalman Filter is one estimator that estimates state changes due to noise from white noise. The final result shows that Kalman Filter is able to improve the GSTAR forecast result. This is shown through simulation results in the form of graphs and clarified with smaller RMSE values.

  2. Comparison of tiered formularies and reference pricing policies: a systematic review.

    Science.gov (United States)

    Morgan, Steve; Hanley, Gillian; Greyson, Devon

    2009-01-01

    To synthesize methodologically comparable evidence from the published literature regarding the outcomes of tiered formularies and therapeutic reference pricing of prescription drugs. We searched the following electronic databases: ABI/Inform, CINAHL, Clinical Evidence, Digital Dissertations & Theses, Evidence-Based Medicine Reviews (which incorporates ACP Journal Club, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Cochrane Methodology Register, Database of Abstracts of Reviews of Effectiveness, Health Technology Assessments and NHS Economic Evaluation Database), EconLit, EMBASE, International Pharmaceutical Abstracts, MEDLINE, PAIS International and PAIS Archive, and the Web of Science. We also searched the reference lists of relevant articles and several grey literature sources. We sought English-language studies published from 1986 to 2007 that examined the effects of either therapeutic reference pricing or tiered formularies, reported on outcomes relevant to patient care and cost-effectiveness, and employed quantitative study designs that included concurrent or historical comparison groups. We abstracted and assessed potentially appropriate articles using a modified version of the data abstraction form developed by the Cochrane Effective Practice and Organisation of Care Group. From an initial list of 2964 citations, 12 citations (representing 11 studies) were deemed eligible for inclusion in our review: 3 studies (reported in 4 articles) of reference pricing and 8 studies of tiered formularies. The introduction of reference pricing was associated with reduced plan spending, switching to preferred medicines, reduced overall drug utilization and short-term increases in the use of physician services. Reference pricing was not associated with adverse health impacts. The introduction of tiered formularies was associated with reduced plan expenditures, greater patient costs and increased rates of non-compliance with

  3. Target Price Accuracy

    Directory of Open Access Journals (Sweden)

    Alexander G. Kerl

    2011-04-01

    Full Text Available This study analyzes the accuracy of forecasted target prices within analysts’ reports. We compute a measure for target price forecast accuracy that evaluates the ability of analysts to exactly forecast the ex-ante (unknown 12-month stock price. Furthermore, we determine factors that explain this accuracy. Target price accuracy is negatively related to analyst-specific optimism and stock-specific risk (measured by volatility and price-to-book ratio. However, target price accuracy is positively related to the level of detail of each report, company size and the reputation of the investment bank. The potential conflicts of interests between an analyst and a covered company do not bias forecast accuracy.

  4. Comparison of Group-Buying Online Auction and Posted Pricing Mechanism in an Uncertain Market

    Institute of Scientific and Technical Information of China (English)

    CHEN Jian; LIU Yunhui; SONG Xiping

    2004-01-01

    Demand uncertainty is a key factor for the seller's decision making, especially in the e-business environment, for the website to sell products through the online auction. In this paper, two kinds of demand uncertainties are considered: the consumer regime uncertainty and the inherent randomness of the market environment. Then, how to use a novel business model and group-buying auction (GBA) is analyzed in such a market environment. Based on the comparison of the GBA and the posted price mechanism, some conditions that favor the GBA are provided.

  5. Who Is the Big Spender? Price Index Effects in Comparisons of Educational Expenditures between Countries and Over Time

    Science.gov (United States)

    Arneberg, Marie; Bowitz, Einar

    2006-01-01

    International comparisons of data on expenditure on education use purchasing power parities for currency conversion and adjustment for price differences between countries to allow for volume comparisons. The resulting indicators are commonly interpreted as differences between countries in input volumes to the education sector-teachers, materials,…

  6. The impact of hazardous industrial facilities on housing prices: A comparison of parametric and semiparametric hedonic price models

    DEFF Research Database (Denmark)

    Grislain-Letrémy, Céline; Katossky, Arthur

    2014-01-01

    The willingness of households to pay for prevention against industrial risks can be revealed by real estate markets. By using very rich microdata, we study housing prices in the vicinity of hazardous industries near three important French cities. We show that the impact of hazardous plants...... to important biases in the estimated value of the impact of hazardous plants on housing values....

  7. Comparison between intensity- duration thresholds and cumulative rainfall thresholds for the forecasting of landslide

    Science.gov (United States)

    Lagomarsino, Daniela; Rosi, Ascanio; Rossi, Guglielmo; Segoni, Samuele; Catani, Filippo

    2014-05-01

    This work makes a quantitative comparison between the results of landslide forecasting obtained using two different rainfall threshold models, one using intensity-duration thresholds and the other based on cumulative rainfall thresholds in an area of northern Tuscany of 116 km2. The first methodology identifies rainfall intensity-duration thresholds by means a software called MaCumBA (Massive CUMulative Brisk Analyzer) that analyzes rain-gauge records, extracts the intensities (I) and durations (D) of the rainstorms associated with the initiation of landslides, plots these values on a diagram, and identifies thresholds that define the lower bounds of the I-D values. A back analysis using data from past events can be used to identify the threshold conditions associated with the least amount of false alarms. The second method (SIGMA) is based on the hypothesis that anomalous or extreme values of rainfall are responsible for landslide triggering: the statistical distribution of the rainfall series is analyzed, and multiples of the standard deviation (σ) are used as thresholds to discriminate between ordinary and extraordinary rainfall events. The name of the model, SIGMA, reflects the central role of the standard deviations in the proposed methodology. The definition of intensity-duration rainfall thresholds requires the combined use of rainfall measurements and an inventory of dated landslides, whereas SIGMA model can be implemented using only rainfall data. These two methodologies were applied in an area of 116 km2 where a database of 1200 landslides was available for the period 2000-2012. The results obtained are compared and discussed. Although several examples of visual comparisons between different intensity-duration rainfall thresholds are reported in the international literature, a quantitative comparison between thresholds obtained in the same area using different techniques and approaches is a relatively undebated research topic.

  8. Comparison between ARIMA and DES Methods of Forecasting Population for Housing Demand in Johor

    OpenAIRE

    Alias Ahmad Rizal; Zainun Noor Yasmin; Abdul Rahman Ismail

    2016-01-01

    Forecasting accuracy is a primary criterion in selecting appropriate method of prediction. Even though there are various methods of forecasting however not all of these methods are able to predict with good accuracy. This paper presents an evaluation of two methods of population forecasting for housing demand. These methods are Autoregressive Integrated Moving Average (ARIMA) and Double Exponential Smoothing (DES). Both of the methods are principally adopting univariate time series analysis w...

  9. A forecasting performance comparison of dynamic factor models based on static and dynamic methods

    Directory of Open Access Journals (Sweden)

    Marra Fabio Della

    2017-03-01

    Full Text Available We present a comparison of the forecasting performances of three Dynamic Factor Models on a large monthly data panel of macroeconomic and financial time series for the UE economy. The first model relies on static principal-component and was introduced by Stock and Watson (2002a, b. The second is based on generalized principal components and it was introduced by Forni, Hallin, Lippi and Reichlin (2000, 2005. The last model has been recently proposed by Forni, Hallin, Lippi and Zaffaroni (2015, 2016. The data panel is split into two parts: the calibration sample, from February 1986 to December 2000, is used to select the most performing specification for each class of models in a in- sample environment, and the proper sample, from January 2001 to November 2015, is used to compare the performances of the selected models in an out-of-sample environment. The metholodogical approach is analogous to Forni, Giovannelli, Lippi and Soccorsi (2016, but also the size of the rolling window is empirically estimated in the calibration process to achieve more robustness. We find that, on the proper sample, the last model is the most performing for the Inflation. However, mixed evidencies appear over the proper sample for the Industrial Production.

  10. A comparison between the ECMWF and COSMO Ensemble Prediction Systems applied to short-term wind power forecasting on real data

    DEFF Research Database (Denmark)

    Alessandrini, S.; Sperati, S.; Pinson, Pierre

    2013-01-01

    together with a single forecast power value for each future time horizon. A comparison between two different ensemble forecasting models, ECMWF EPS (Ensemble Prediction System in use at the European Centre for Medium-Range Weather Forecasts) and COSMO-LEPS (Limited-area Ensemble Prediction System developed...... ahead forecast horizon. A statistical calibration of the ensemble wind speed members based on the use of past wind speed measurements is explained. The two models are compared using common verification indices and diagrams. The higher horizontal resolution model (COSMO-LEPS) shows slightly better...

  11. Price comparison of high-cost originator medicines in European countries.

    Science.gov (United States)

    Vogler, Sabine; Zimmermann, Nina; Babar, Zaheer-Ud-Din

    2017-04-01

    In recent years, high-cost medicines have increasingly been challenging the public health budget in all countries including high-income economies. In this context, this study aims to survey, analyze and compare prices of medicines that likely contribute to high expenditure for the public payers in high-income countries. We chose the following 16 European countries: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, the Netherlands, Portugal, Sweden, Slovakia, Spain and United Kingdom. The ex-factory price data of 30 medicines in these countries were collected in national databases accessible through the Pharmaceutical Price Information (PPI) service of Gesundheit Österreich GmbH (Austrian Public Health Institute). The ex-factory prices (median) per unit (e.g. per tablet, vial) ranged from 10.67 cent (levodopa + decarboxylase inhibitor) to 17,000 euro (ipilimumab). A total of 53% of the medicines surveyed had a unit ex-factory price (median) above 200 Euro. For two thirds of the medicines, price differences between the highest-priced country and lowest-priced country ranged between 25 and 100%; the remaining medicines, mainly low-priced medicines, had higher price differential, up to 251%. Medicines with unit prices of a few euros or less were medicines for the treatment of diseases in the nervous system (anti-depressants, medicines to treat Parkinson and for the management of neuropathic pain), of obstructive airway diseases and cardio-vascular medicines (lipid modifying agents). High-priced medicines were particularly cancer medicines. Medicine prices of Greece, Hungary, Slovakia and UK were frequently at the lower end, German and Swedish, as well as Danish and Irish prices at the upper end. For high-priced medicines, actual paid prices are likely to be lower due to confidential discounts and similar funding arrangements between industry and public payers. Pricing authorities refer to the higher undiscounted prices when they use

  12. Profit, productivity, and price differential: an international performance comparison of the natural gas transportation industry

    International Nuclear Information System (INIS)

    Jeong-Dong Lee; Sung-Bae Park; Tai-Yoo Kim

    1999-01-01

    The objective of this paper is to evaluate the performance of natural gas transportation utilities, focusing on the three-key strategic performance measures of profit, productivity, and price differential. We propose a methodology that expresses the three performance measures in a unified single equation using the Edgeworth index. The proposed methodology is applied to an international comparison of the performance of 28 natural gas transportation utilities operating in eight countries in which the business environments differ greatly. The empirical results show the possible causes of performance differences and may shed some light on the direction of regulatory policy, especially for developing countries that have relatively short histories in the natural gas industry, such as Korea. (author)

  13. Comparison between ARIMA and DES Methods of Forecasting Population for Housing Demand in Johor

    Directory of Open Access Journals (Sweden)

    Alias Ahmad Rizal

    2016-01-01

    Full Text Available Forecasting accuracy is a primary criterion in selecting appropriate method of prediction. Even though there are various methods of forecasting however not all of these methods are able to predict with good accuracy. This paper presents an evaluation of two methods of population forecasting for housing demand. These methods are Autoregressive Integrated Moving Average (ARIMA and Double Exponential Smoothing (DES. Both of the methods are principally adopting univariate time series analysis which uses past and present data for forecasting. Secondary data obtained from Department of Statistics, Malaysia was used to forecast population for housing demand in Johor. Forecasting processes had generated 14 models to each of the methods and these models where evaluated using Mean Absolute Percentage Error (MAPE. It was found that 14 of Double Exponential Smoothing models and also 14 of ARIMA models had resulted to 1.674% and 5.524% of average MAPE values respectively. Hence, the Double Exponential Smoothing method outperformed the ARIMA method by reducing 4.00 % in forecasting model population for Johor state. These findings help researchers and government agency in selecting appropriate forecasting model for housing demand.

  14. Comparison of Adaline and Multiple Linear Regression Methods for Rainfall Forecasting

    Science.gov (United States)

    Sutawinaya, IP; Astawa, INGA; Hariyanti, NKD

    2018-01-01

    Heavy rainfall can cause disaster, therefore need a forecast to predict rainfall intensity. Main factor that cause flooding is there is a high rainfall intensity and it makes the river become overcapacity. This will cause flooding around the area. Rainfall factor is a dynamic factor, so rainfall is very interesting to be studied. In order to support the rainfall forecasting, there are methods that can be used from Artificial Intelligence (AI) to statistic. In this research, we used Adaline for AI method and Regression for statistic method. The more accurate forecast result shows the method that used is good for forecasting the rainfall. Through those methods, we expected which is the best method for rainfall forecasting here.

  15. Comparison of Statistical Post-Processing Methods for Probabilistic Wind Speed Forecasting

    Science.gov (United States)

    Han, Keunhee; Choi, JunTae; Kim, Chansoo

    2018-02-01

    In this study, the statistical post-processing methods that include bias-corrected and probabilistic forecasts of wind speed measured in PyeongChang, which is scheduled to host the 2018 Winter Olympics, are compared and analyzed to provide more accurate weather information. The six post-processing methods used in this study are as follows: mean bias-corrected forecast, mean and variance bias-corrected forecast, decaying averaging forecast, mean absolute bias-corrected forecast, and the alternative implementations of ensemble model output statistics (EMOS) and Bayesian model averaging (BMA) models, which are EMOS and BMA exchangeable models by assuming exchangeable ensemble members and simplified version of EMOS and BMA models. Observations for wind speed were obtained from the 26 stations in PyeongChang and 51 ensemble member forecasts derived from the European Centre for Medium-Range Weather Forecasts (ECMWF Directorate, 2012) that were obtained between 1 May 2013 and 18 March 2016. Prior to applying the post-processing methods, reliability analysis was conducted by using rank histograms to identify the statistical consistency of ensemble forecast and corresponding observations. Based on the results of our study, we found that the prediction skills of probabilistic forecasts of EMOS and BMA models were superior to the biascorrected forecasts in terms of deterministic prediction, whereas in probabilistic prediction, BMA models showed better prediction skill than EMOS. Even though the simplified version of BMA model exhibited best prediction skill among the mentioned six methods, the results showed that the differences of prediction skills between the versions of EMOS and BMA were negligible.

  16. Forecasting Housing Approvals in Australia: Do Forecasters Herd?

    DEFF Research Database (Denmark)

    Stadtmann, Georg; Pierdzioch; Rülke

    2012-01-01

    Price trends in housing markets may reflect herding of market participants. A natural question is whether such herding, to the extent that it occurred, reflects herding in forecasts of professional forecasters. Using more than 6,000 forecasts of housing approvals for Australia, we did not find...

  17. Competitive Pricing by a Price Leader

    OpenAIRE

    Abhik Roy; Dominique M. Hanssens; Jagmohan S. Raju

    1994-01-01

    We examine the problem of pricing in a market where one brand acts as a price leader. We develop a procedure to estimate a leader's price rule, which is optimal given a sales target objective, and allows for the inclusion of demand forecasts. We illustrate our estimation procedure by calibrating this optimal price rule for both the leader and the follower using data on past sales and prices from the mid-size sedan segment of the U.S. automobile market. Our results suggest that a leader-follow...

  18. Energy markets and price relations

    International Nuclear Information System (INIS)

    Bergendahl, P.A.

    1986-10-01

    The aim of the report is to elucidate the way and extent of the dependence of the price of different energy species of one another and particularly of crude oil prices. Oil, coal and natural gas can substitute each other at many applications. The prices are dependent on mining, processing and transporting. Forecasting of prices and future trends are discussed

  19. A Comparison of Pricing Strategies for Bibliographical Databases on CDROM and Equivalent Printed Products.

    Science.gov (United States)

    Rowley, Jennifer; Butcher, David

    1994-01-01

    Considers comparative prices for bibliographic data on CD-ROM and in print. Topics addressed include differences in the nature of bibliographic data in the two media, the relative complexities of pricing structure, varying policies on network pricing, and standardization of the licensing arrangement. (KRN)

  20. Forecasting ocean wave energy: A Comparison of the ECMWF wave model with time series methods

    DEFF Research Database (Denmark)

    Reikard, Gordon; Pinson, Pierre; Bidlot, Jean

    2011-01-01

    Recently, the technology has been developed to make wave farms commercially viable. Since electricity is perishable, utilities will be interested in forecasting ocean wave energy. The horizons involved in short-term management of power grids range from as little as a few hours to as long as several...... days. In selecting a method, the forecaster has a choice between physics-based models and statistical techniques. A further idea is to combine both types of models. This paper analyzes the forecasting properties of a well-known physics-based model, the European Center for Medium-Range Weather Forecasts...... (ECMWF) Wave Model, and two statistical techniques, time-varying parameter regressions and neural networks. Thirteen data sets at locations in the Atlantic and Pacific Oceans and the Gulf of Mexico are tested. The quantities to be predicted are the significant wave height, the wave period, and the wave...

  1. Seasonal forecasting of hydrological drought in the Limpopo Basin: a comparison of statistical methods

    Science.gov (United States)

    Seibert, Mathias; Merz, Bruno; Apel, Heiko

    2017-03-01

    The Limpopo Basin in southern Africa is prone to droughts which affect the livelihood of millions of people in South Africa, Botswana, Zimbabwe and Mozambique. Seasonal drought early warning is thus vital for the whole region. In this study, the predictability of hydrological droughts during the main runoff period from December to May is assessed using statistical approaches. Three methods (multiple linear models, artificial neural networks, random forest regression trees) are compared in terms of their ability to forecast streamflow with up to 12 months of lead time. The following four main findings result from the study. 1. There are stations in the basin at which standardised streamflow is predictable with lead times up to 12 months. The results show high inter-station differences of forecast skill but reach a coefficient of determination as high as 0.73 (cross validated). 2. A large range of potential predictors is considered in this study, comprising well-established climate indices, customised teleconnection indices derived from sea surface temperatures and antecedent streamflow as a proxy of catchment conditions. El Niño and customised indices, representing sea surface temperature in the Atlantic and Indian oceans, prove to be important teleconnection predictors for the region. Antecedent streamflow is a strong predictor in small catchments (with median 42 % explained variance), whereas teleconnections exert a stronger influence in large catchments. 3. Multiple linear models show the best forecast skill in this study and the greatest robustness compared to artificial neural networks and random forest regression trees, despite their capabilities to represent nonlinear relationships. 4. Employed in early warning, the models can be used to forecast a specific drought level. Even if the coefficient of determination is low, the forecast models have a skill better than a climatological forecast, which is shown by analysis of receiver operating characteristics

  2. Comparison of short term rainfall forecasts for model based flow prediction in urban drainage systems

    DEFF Research Database (Denmark)

    Thorndahl, Søren; Poulsen, Troels Sander; Bøvith, Thomas

    2012-01-01

    Forecast based flow prediction in drainage systems can be used to implement real time control of drainage systems. This study compares two different types of rainfall forecasts – a radar rainfall extrapolation based nowcast model and a numerical weather prediction model. The models are applied...... performance of the system is found using the radar nowcast for the short leadtimes and weather model for larger lead times....

  3. Comparison of short-term rainfall forecasts for modelbased flow prediction in urban drainage systems

    DEFF Research Database (Denmark)

    Thorndahl, Søren; Ahm, Malte; Nielsen, Jesper Ellerbek

    2013-01-01

    Forecast-based flow prediction in drainage systems can be used to implement real-time control of drainage systems. This study compares two different types of rainfall forecast - a radar rainfall extrapolation-based nowcast model and a numerical weather prediction model. The models are applied...... performance of the system is found using the radar nowcast for the short lead times and the weather model for larger lead times....

  4. Comparison of electricity prices in major North American cities : rates effective May 1, 2001

    International Nuclear Information System (INIS)

    2002-01-01

    This report presents the major findings of a comparative evaluation of electricity prices in major North American cities, by customer category, based on rates in effect on May 1, 2001. Besides Hydro-Quebec, the comparison includes 21 utilities, of which 11 serve the major cities across Canada and 10 utilities located in 10 American states. Hydro-Quebec is one of the largest electric utilities in North America with one of the most extensive systems. Of the 190 billion kilowatt-hours delivered annually, 37 billion are exported to neighbouring provinces in Canada and to the United States. 93 per cent of the generating facilities are hydroelectric. The report covers consumption levels, taxes, optional programs, location, time-of-use rates, exchange rates, and other factors that affect electricity prices such as fuel adjustment clauses for utilities that produce electricity in thermal power plants. It was noted that Hydro-Quebec has frozen its rates until April 2002, keeping them at the same level as those set on May 1, 1998. The report showed that Hydro-Quebec has maintained a competitive position at a level similar to or better than previous years. Three of the 11 Canadian utilities implemented rate increases between May 1, 2000 and May 1, 2001, and 2 utilities reduced their rates. Two utilities modified their rate structure following deregulation. During the same time period, 3 of the 10 utilities in the United States increased their rates, and one reduced them. The rates in the United States varied regardless of increases or freezes because many utilities had incorporated adjustment clauses. Quebec residential consumers enjoy the second lowest rates in North America. Only Winnipeg ranked higher. Hydro-Quebec has also remained competitive when it comes to small, medium and large power customers. It ranked fifth for small power customers, third for medium power customers, and third for large power customers. tabs., figs., appendices

  5. Foreign currency rate forecasting using neural networks

    Science.gov (United States)

    Pandya, Abhijit S.; Kondo, Tadashi; Talati, Amit; Jayadevappa, Suryaprasad

    2000-03-01

    Neural networks are increasingly being used as a forecasting tool in many forecasting problems. This paper discusses the application of neural networks in predicting daily foreign exchange rates between the USD, GBP as well as DEM. We approach the problem from a time-series analysis framework - where future exchange rates are forecasted solely using past exchange rates. This relies on the belief that the past prices and future prices are very close related, and interdependent. We present the result of training a neural network with historical USD-GBP data. The methodology used in explained, as well as the training process. We discuss the selection of inputs to the network, and present a comparison of using the actual exchange rates and the exchange rate differences as inputs. Price and rate differences are the preferred way of training neural network in financial applications. Results of both approaches are present together for comparison. We show that the network is able to learn the trends in the exchange rate movements correctly, and present the results of the prediction over several periods of time.

  6. The price may not be right: the value of comparison shopping for prescription drugs.

    Science.gov (United States)

    Arora, Sanjay; Sood, Neeraj; Terp, Sophie; Joyce, Geoffrey

    2017-07-01

    To measure variations in drug prices across and within zip codes that may reveal simple strategies to improve patients' access to prescribed medications. We compared drug prices at different types of pharmacies across and within local markets. In-store prices were compared with a Web-based service providing discount coupons for prescription medications. Prices were collected for 2 generic antibiotics because most patients have limited experience with them and are less likely to know the price ranges for them. Drug prices were obtained via telephone from 528 pharmacies in Los Angeles (LA) County, California, from July to August 2014. Online prices were collected from GoodRx, a popular Web-based service that aggregates available discounts and directly negotiates with retail outlets. Drug prices found at independent pharmacies and by using discount coupons available online were lower on average than at grocery, big-box, or chain drug stores for 2 widely prescribed antibiotics. The lowest-price prescription was offered at a grocery, big-box, or chain drug store in 6% of zip codes within the LA County area. Drug prices varied dramatically within a zip code, however, and were less expensive in lower-income areas. The average price difference within a zip code was $52 for levofloxacin and $17 for azithromycin. Price shopping for medications within a small geographic area can yield considerable cost savings for the uninsured and consumers in high-deductible health plans with high negotiated prices. Clinicians and patient advocates have an incentive to convey this information to patients to improve adherence to prescribed medicines and lower the financial burden of purchasing prescription drugs.

  7. Which is the better forecasting model? A comparison between HAR-RV and multifractality volatility

    Science.gov (United States)

    Ma, Feng; Wei, Yu; Huang, Dengshi; Chen, Yixiang

    2014-07-01

    In this paper, by taking the 5-min high frequency data of the Shanghai Composite Index as example, we compare the forecasting performance of HAR-RV and Multifractal volatility, Realized volatility, Realized Bipower Variation and their corresponding short memory model with rolling windows forecasting method and the Model Confidence Set which is proved superior to SPA test. The empirical results show that, for six loss functions, HAR-RV outperforms other models. Moreover, to make the conclusions more precise and robust, we use the MCS test to compare the performance of their logarithms form models, and find that the HAR-log(RV) has a better performance in predicting future volatility. Furthermore, by comparing the two models of HAR-RV and HAR-log(RV), we conclude that, in terms of performance forecasting, the HAR-log(RV) model is the best model among models we have discussed in this paper.

  8. Forecasting method for global radiation time series without training phase: Comparison with other well-known prediction methodologies

    International Nuclear Information System (INIS)

    Voyant, Cyril; Motte, Fabrice; Fouilloy, Alexis; Notton, Gilles; Paoli, Christophe; Nivet, Marie-Laure

    2017-01-01

    Integration of unpredictable renewable energy sources into electrical networks intensifies the complexity of the grid management due to their intermittent and unforeseeable nature. Because of the strong increase of solar power generation the prediction of solar yields becomes more and more important. Electrical operators need an estimation of the future production. For nowcasting and short term forecasting, the usual technics based on machine learning need large historical data sets of good quality during the training phase of predictors. However data are not always available and induce an advanced maintenance of meteorological stations, making the method inapplicable for poor instrumented or isolated sites. In this work, we propose intuitive methodologies based on the Kalman filter use (also known as linear quadratic estimation), able to predict a global radiation time series without the need of historical data. The accuracy of these methods is compared to other classical data driven methods, for different horizons of prediction and time steps. The proposed approach shows interesting capabilities allowing to improve quasi-systematically the prediction. For one to 10 h horizons Kalman model performances are competitive in comparison to more sophisticated models such as ANN which require both consistent historical data sets and computational resources. - Highlights: • Solar radiation forecasting with time series formalism. • Trainless approach compared to machine learning methods. • Very simple method dedicated to solar irradiation forecasting with high accuracy.

  9. A comparison of cost-based pricing rules for natural gas distribution utilities

    International Nuclear Information System (INIS)

    Klein, C.C.

    1993-01-01

    Partial-equilibrium social welfare deadweight losses under uniform Ramsey pricing, a cost allocation pricing method, and the actual average revenues by customer class for two natural gas distribution utilities are calculated and compared. Marginal cost estimates are derived from a multiple-output translog variable cost function and used, along with three sets of demand elasticities, to generate the Ramsey prices and welfare losses. The actual and cost-allocation prices are taken directly from rate case files. The largest social welfare losses are associated with the cost-allocation rule, as high as 10-25% of revenue, despite suggestions in the literature to the contrary. (Author)

  10. A New Approach to Forecasting Exchange Rates

    OpenAIRE

    Kenneth W Clements; Yihui Lan

    2006-01-01

    Building on purchasing power parity theory, this paper proposes a new approach to forecasting exchange rates using the Big Mac data from The Economist magazine. Our approach is attractive in three aspects. Firstly, it uses easily-available Big Mac prices as input. These prices avoid several serious problems associated with broad price indexes, such as the CPI, that are used in conventional PPP studies. Secondly, this approach provides real-time exchange-rate forecasts at any forecast horizon....

  11. A comparison of regression algorithms for wind speed forecasting at Alexander Bay

    CSIR Research Space (South Africa)

    Botha, Nicolene

    2016-12-01

    Full Text Available to forecast 1 to 24 hours ahead, in hourly intervals. Predictions are performed on a wind speed time series with three machine learning regression algorithms, namely support vector regression, ordinary least squares and Bayesian ridge regression. The resulting...

  12. A comparison of the stochastic and machine learning approaches in hydrologic time series forecasting

    Science.gov (United States)

    Kim, T.; Joo, K.; Seo, J.; Heo, J. H.

    2016-12-01

    Hydrologic time series forecasting is an essential task in water resources management and it becomes more difficult due to the complexity of runoff process. Traditional stochastic models such as ARIMA family has been used as a standard approach in time series modeling and forecasting of hydrological variables. Due to the nonlinearity in hydrologic time series data, machine learning approaches has been studied with the advantage of discovering relevant features in a nonlinear relation among variables. This study aims to compare the predictability between the traditional stochastic model and the machine learning approach. Seasonal ARIMA model was used as the traditional time series model, and Random Forest model which consists of decision tree and ensemble method using multiple predictor approach was applied as the machine learning approach. In the application, monthly inflow data from 1986 to 2015 of Chungju dam in South Korea were used for modeling and forecasting. In order to evaluate the performances of the used models, one step ahead and multi-step ahead forecasting was applied. Root mean squared error and mean absolute error of two models were compared.

  13. Comparison of radar and numerical weather model rainfall forecasts in the perspective of urban flood prediction

    DEFF Research Database (Denmark)

    Lovring, Maite Monica; Löwe, Roland; Courdent, Vianney Augustin Thomas

    An early flood warning system has been developed for urban catchments and is currently running in online operation in Copenhagen. The system is highly dependent on the quality of rainfall forecast inputs. An investigation of precipitation inputs from Radar Nowcast (RN), Numerical Weather Prediction...

  14. PRICE AND PRICING STRATEGIES

    OpenAIRE

    SUCIU Titus

    2013-01-01

    In individual companies, price is one significant factor in achieving marketing success. In many purchase situations, price can be of great importance to customers. Marketers must establish pricing strategies that are compatible with the rest of the marketing mix. Management should decide whether to charge the same price to all similar buyers of identical quantities of a product (a one-price strategy) or to set different prices (a flexible price strategy). Many organizations, especially retai...

  15. Stock price prediction using geometric Brownian motion

    Science.gov (United States)

    Farida Agustini, W.; Restu Affianti, Ika; Putri, Endah RM

    2018-03-01

    Geometric Brownian motion is a mathematical model for predicting the future price of stock. The phase that done before stock price prediction is determine stock expected price formulation and determine the confidence level of 95%. On stock price prediction using geometric Brownian Motion model, the algorithm starts from calculating the value of return, followed by estimating value of volatility and drift, obtain the stock price forecast, calculating the forecast MAPE, calculating the stock expected price and calculating the confidence level of 95%. Based on the research, the output analysis shows that geometric Brownian motion model is the prediction technique with high rate of accuracy. It is proven with forecast MAPE value ≤ 20%.

  16. The evolving price of household LED lamps: Recent trends and historical comparisons for the US market

    Energy Technology Data Exchange (ETDEWEB)

    Gerke, Brian F.; Ngo, Allison T.; Alstone, Andrea L.; Fisseha, Kibret S.

    2014-10-14

    In recent years, household LED light bulbs (LED A lamps) have undergone a dramatic price decline. Since late 2011, we have been collecting data, on a weekly basis, for retail offerings of LED A lamps on the Internet. The resulting data set allows us to track the recent price decline in detail. LED A lamp prices declined roughly exponentially with time in 2011-2014, with decline rates of 28percent to 44percent per year depending on lumen output, and with higher-lumen lamps exhibiting more rapid price declines. By combining the Internet price data with publicly available lamp shipments indices for the US market, it is also possible to correlate LED A lamp prices against cumulative production, yielding an experience curve for LED A lamps. In 2012-2013, LED A lamp prices declined by 20-25percent for each doubling in cumulative shipments. Similar analysis of historical data for other lighting technologies reveals that LED prices have fallen significantly more rapidly with cumulative production than did their technological predecessors, which exhibited a historical decline of 14-15percent per doubling of production.

  17. Charging versus rewarding : A comparison of road-pricing and rewarding peak avoidance in the Netherlands

    NARCIS (Netherlands)

    Tillema, Taede; Ben-Elia, Eran; Ettema, Dick; van Delden, Janet

    The aim of this paper is to compare two congestion management schemes road-pricing and peak avoidance rewarding and their impact on commuter behaviour, based on two studies that were conducted in the Netherlands. The road-pricing study is based on stated preference data, whereas the study involving

  18. Prescription Drugs: Comparison of DOD, Medicaid, and Medicare Part D Retail Reimbursement Prices

    Science.gov (United States)

    2014-06-01

    information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215...Medicaid Services DIR direct and indirect remuneration DOD Department of Defense FCP federal ceiling price HCPCS Healthcare Common Procedure...prices, we used PDTS data from DOD; unit rebate amount (URA) and CMS-64 data from Medicaid; and the 2010 Direct and Indirect Remuneration (DIR

  19. Comparison of filtering methods for the modeling and retrospective forecasting of influenza epidemics.

    Directory of Open Access Journals (Sweden)

    Wan Yang

    2014-04-01

    Full Text Available A variety of filtering methods enable the recursive estimation of system state variables and inference of model parameters. These methods have found application in a range of disciplines and settings, including engineering design and forecasting, and, over the last two decades, have been applied to infectious disease epidemiology. For any system of interest, the ideal filter depends on the nonlinearity and complexity of the model to which it is applied, the quality and abundance of observations being entrained, and the ultimate application (e.g. forecast, parameter estimation, etc.. Here, we compare the performance of six state-of-the-art filter methods when used to model and forecast influenza activity. Three particle filters--a basic particle filter (PF with resampling and regularization, maximum likelihood estimation via iterated filtering (MIF, and particle Markov chain Monte Carlo (pMCMC--and three ensemble filters--the ensemble Kalman filter (EnKF, the ensemble adjustment Kalman filter (EAKF, and the rank histogram filter (RHF--were used in conjunction with a humidity-forced susceptible-infectious-recovered-susceptible (SIRS model and weekly estimates of influenza incidence. The modeling frameworks, first validated with synthetic influenza epidemic data, were then applied to fit and retrospectively forecast the historical incidence time series of seven influenza epidemics during 2003-2012, for 115 cities in the United States. Results suggest that when using the SIRS model the ensemble filters and the basic PF are more capable of faithfully recreating historical influenza incidence time series, while the MIF and pMCMC do not perform as well for multimodal outbreaks. For forecast of the week with the highest influenza activity, the accuracies of the six model-filter frameworks are comparable; the three particle filters perform slightly better predicting peaks 1-5 weeks in the future; the ensemble filters are more accurate predicting peaks in

  20. Prices and Price Setting

    NARCIS (Netherlands)

    R.P. Faber (Riemer)

    2010-01-01

    textabstractThis thesis studies price data and tries to unravel the underlying economic processes of why firms have chosen these prices. It focuses on three aspects of price setting. First, it studies whether the existence of a suggested price has a coordinating effect on the prices of firms.

  1. Estimating the commodity market price of risk for energy prices

    International Nuclear Information System (INIS)

    Kolos, Sergey P.; Ronn, Ehud I.

    2008-01-01

    The purpose of this paper is to estimate the ''market price of risk'' (MPR) for energy commodities, the ratio of expected return to standard deviation. The MPR sign determines whether energy forward prices are upward- or downward-biased predictors of expected spot prices. We estimate MPRs using spot and futures prices, while accounting for the Samuelson effect. We find long-term MPRs generally positive and short-term negative, consistent with positive energy betas and hedging, respectively. In spot electricity markets, MPRs in Day-Ahead Prices agree with short-dated futures. Our results relate risk premia to informed hedging decisions, and futures prices to forecast/expected prices. (author)

  2. A SOM clustering pattern sequence-based next symbol prediction method for day-ahead direct electricity load and price forecasting

    International Nuclear Information System (INIS)

    Jin, Cheng Hao; Pok, Gouchol; Lee, Yongmi; Park, Hyun-Woo; Kim, Kwang Deuk; Yun, Unil; Ryu, Keun Ho

    2015-01-01

    Highlights: • A novel pattern sequence-based direct time series forecasting method was proposed. • Due to the use of SOM’s topology preserving property, only SOM can be applied. • SCPSNSP only deals with the cluster patterns not each specific time series value. • SCPSNSP performs better than recently developed forecasting algorithms. - Abstract: In this paper, we propose a new day-ahead direct time series forecasting method for competitive electricity markets based on clustering and next symbol prediction. In the clustering step, pattern sequence and their topology relations are obtained from self organizing map time series clustering. In the next symbol prediction step, with each cluster label in the pattern sequence represented as a pair of its topologically identical coordinates, artificial neural network is used to predict the topological coordinates of next day by training the relationship between previous daily pattern sequence and its next day pattern. According to the obtained topology relations, the nearest nonzero hits pattern is assigned to next day so that the whole time series values can be directly forecasted from the assigned cluster pattern. The proposed method was evaluated on Spanish, Australian and New York electricity markets and compared with PSF and some of the most recently published forecasting methods. Experimental results show that the proposed method outperforms the best forecasting methods at least 3.64%

  3. Comparisons of forecasting for hepatitis in Guangxi Province, China by using three neural networks models

    Directory of Open Access Journals (Sweden)

    Ruijing Gan

    2016-11-01

    Full Text Available This study compares and evaluates the prediction of hepatitis in Guangxi Province, China by using back propagation neural networks based genetic algorithm (BPNN-GA, generalized regression neural networks (GRNN, and wavelet neural networks (WNN. In order to compare the results of forecasting, the data obtained from 2004 to 2013 and 2014 were used as modeling and forecasting samples, respectively. The results show that when the small data set of hepatitis has seasonal fluctuation, the prediction result by BPNN-GA will be better than the two other methods. The WNN method is suitable for predicting the large data set of hepatitis that has seasonal fluctuation and the same for the GRNN method when the data increases steadily.

  4. Value-based pricing

    OpenAIRE

    Netseva-Porcheva Tatyana

    2010-01-01

    The main aim of the paper is to present the value-based pricing. Therefore, the comparison between two approaches of pricing is made - cost-based pricing and value-based pricing. The 'Price sensitively meter' is presented. The other topic of the paper is the perceived value - meaning of the perceived value, the components of perceived value, the determination of perceived value and the increasing of perceived value. In addition, the best company strategies in matrix 'value-cost' are outlined. .

  5. Feed-in tariff and market electricity price comparison. The case of cogeneration units in Croatia

    International Nuclear Information System (INIS)

    Uran, Vedran; Krajcar, Slavko

    2009-01-01

    In August 2007, the Government of the Republic of Croatia instituted a feed-in tariff system, requiring the Croatian Electricity Market Operator (HROTE) to off-take the electricity produced from renewable energy sources or cogeneration units fueled by natural gas. Analysis of the off-take electricity price range, which depends on the net electrical output and electricity market trends, indicates that it is more cost effective for cogeneration units greater than 1 MW to sell their electricity on the exchange market. This was confirmed by developing a mathematical model to calculate the cost-effectiveness ratio of a cogeneration unit. This ratio represents the relation between the profit spread, i.e. the difference between the profit generated from selling the electricity on the exchange market and the profit made from dispatching the electricity to HROTE, as well as the total investment costs. The model can be applied for changes in certain parameters, such as the net electrical output, volatility and spot electricity price. The Monte Carlo method is used to obtain the most probable cost-effectiveness ratio and average future electricity price. Together with these two economic parameters and market price analysis, it is possible to calculate and calibrate an acceptable off-take electricity price. (author)

  6. An empirical comparison of alternate regime-switching models for electricity spot prices

    Energy Technology Data Exchange (ETDEWEB)

    Janczura, Joanna [Hugo Steinhaus Center, Institute of Mathematics and Computer Science, Wroclaw University of Technology, 50-370 Wroclaw (Poland); Weron, Rafal [Institute of Organization and Management, Wroclaw University of Technology, 50-370 Wroclaw (Poland)

    2010-09-15

    One of the most profound features of electricity spot prices are the price spikes. Markov regime-switching (MRS) models seem to be a natural candidate for modeling this spiky behavior. However, in the studies published so far, the goodness-of-fit of the proposed models has not been a major focus. While most of the models were elegant, their fit to empirical data has either been not examined thoroughly or the signs of a bad fit ignored. With this paper we want to fill the gap. We calibrate and test a range of MRS models in an attempt to find parsimonious specifications that not only address the main characteristics of electricity prices but are statistically sound as well. We find that the best structure is that of an independent spike 3-regime model with time-varying transition probabilities, heteroscedastic diffusion-type base regime dynamics and shifted spike regime distributions. Not only does it allow for a seasonal spike intensity throughout the year and consecutive spikes or price drops, which is consistent with market observations, but also exhibits the 'inverse leverage effect' reported in the literature for spot electricity prices. (author)

  7. An empirical comparison of alternate regime-switching models for electricity spot prices

    International Nuclear Information System (INIS)

    Janczura, Joanna; Weron, Rafal

    2010-01-01

    One of the most profound features of electricity spot prices are the price spikes. Markov regime-switching (MRS) models seem to be a natural candidate for modeling this spiky behavior. However, in the studies published so far, the goodness-of-fit of the proposed models has not been a major focus. While most of the models were elegant, their fit to empirical data has either been not examined thoroughly or the signs of a bad fit ignored. With this paper we want to fill the gap. We calibrate and test a range of MRS models in an attempt to find parsimonious specifications that not only address the main characteristics of electricity prices but are statistically sound as well. We find that the best structure is that of an independent spike 3-regime model with time-varying transition probabilities, heteroscedastic diffusion-type base regime dynamics and shifted spike regime distributions. Not only does it allow for a seasonal spike intensity throughout the year and consecutive spikes or price drops, which is consistent with market observations, but also exhibits the 'inverse leverage effect' reported in the literature for spot electricity prices. (author)

  8. The Value of Renewable Energy as a Hedge Against Fuel Price Risk: Analytic Contributions from Economic and Finance Theory

    Energy Technology Data Exchange (ETDEWEB)

    Bolinger, Mark A; Wiser, Ryan

    2008-09-15

    natural gas in the United States over a relatively brief period. Perhaps of most concern is that this dramatic price increase was largely unforeseen. Figure 2 compares the EIA's natural gas wellhead price forecast from each year's Annual Energy Outlook (AEO) going back to 1985 against the average US wellhead price that actually transpired. As shown, our forecasting abilities have proven rather dismal over time, as over-forecasts made in the late 1980's eventually yielded to under-forecasts that have persisted to this day. This historical experience demonstrates that little weight should be placed on any one forecast of future natural gas prices, and that a broad range of future price conditions ought to be considered in planning and investment decisions. Against this backdrop of high, volatile, and unpredictable natural gas prices, increasing the market penetration of renewable generation such as wind, solar, and geothermal power may provide economic benefits to ratepayers by displacing gas-fired generation. These benefits may manifest themselves in several ways. First, the displacement of natural gas-fired generation by increased renewable generation reduces ratepayer exposure to natural gas price risk--i.e., the risk that future gas prices (and by extension future electricity prices) may end up markedly different than expected. Second, this displacement reduces demand for natural gas among gas-fired generators, which, all else equal, will put downward pressure on natural gas prices. Lower natural gas prices in turn benefit both electric ratepayers and other end-users of natural gas. Using analytic approaches that build upon, yet differ from, the past work of others, including Awerbuch (1993, 1994, 2003), Kahn and Stoft (1993), and Humphreys and McClain (1998), this chapter explores each of these two potential 'hedging' benefits of renewable electricity. Though we do not seek to judge whether these two specific benefits outweigh any incremental

  9. Hierarchical Bayesian Spatio Temporal Model Comparison on the Earth Trapped Particle Forecast

    International Nuclear Information System (INIS)

    Suparta, Wayan; Gusrizal

    2014-01-01

    We compared two hierarchical Bayesian spatio temporal (HBST) results, Gaussian process (GP) and autoregressive (AR) models, on the Earth trapped particle forecast. Two models were employed on the South Atlantic Anomaly (SAA) region. Electron of >30 keV (mep0e1) from National Oceanic and Atmospheric Administration (NOAA) 15-18 satellites data was chosen as the particle modeled. We used two weeks data to perform the model fitting on a 5°x5° grid of longitude and latitude, and 31 August 2007 was set as the date of forecast. Three statistical validations were performed on the data, i.e. the root mean square error (RMSE), mean absolute percentage error (MAPE) and bias (BIAS). The statistical analysis showed that GP model performed better than AR with the average of RMSE = 0.38 and 0.63, MAPE = 11.98 and 17.30, and BIAS = 0.32 and 0.24, for GP and AR, respectively. Visual validation on both models with the NOAA map's also confirmed the superior of the GP than the AR. The variance of log flux minimum = 0.09 and 1.09, log flux maximum = 1.15 and 1.35, and in successively represents GP and AR

  10. A comparison of two typical multicyclic models used to forecast the world's conventional oil production

    International Nuclear Information System (INIS)

    Wang Jianliang; Feng Lianyong; Zhao Lin; Snowden, Simon; Wang Xu

    2011-01-01

    This paper introduces two typical multicyclic models: the Hubbert model and the Generalized Weng model. The model-solving process of the two is expounded, and it provides the basis for an empirical analysis of the world's conventional oil production. The results for both show that the world's conventional oil (crude+NGLs) production will reach its peak in 2011 with a production of 30 billion barrels (Gb). In addition, the forecasting effects of these two models, given the same URR are compared, and the intrinsic characteristics of these two models are analyzed. This demonstrates that for specific criteria the multicyclic Generalized Weng model is an improvement on the multicyclic Hubbert model. Finally, based upon the resultant forecast for the world's conventional oil, some suggestions are proposed for China's policy makers. - Highlights: ► Hubbert model and Generalized Weng model are introduced and compared in this article. ► We conclude each model's characteristic and scopes and conditions of applicable. ► We get the same peak production and time of world's oil by applying two models. ► Multicyclic Generalized Weng model is proven slightly better than Hubbert model.

  11. A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series

    Science.gov (United States)

    Wang, Wen-Chuan; Chau, Kwok-Wing; Cheng, Chun-Tian; Qiu, Lin

    2009-08-01

    SummaryDeveloping a hydrological forecasting model based on past records is crucial to effective hydropower reservoir management and scheduling. Traditionally, time series analysis and modeling is used for building mathematical models to generate hydrologic records in hydrology and water resources. Artificial intelligence (AI), as a branch of computer science, is capable of analyzing long-series and large-scale hydrological data. In recent years, it is one of front issues to apply AI technology to the hydrological forecasting modeling. In this paper, autoregressive moving-average (ARMA) models, artificial neural networks (ANNs) approaches, adaptive neural-based fuzzy inference system (ANFIS) techniques, genetic programming (GP) models and support vector machine (SVM) method are examined using the long-term observations of monthly river flow discharges. The four quantitative standard statistical performance evaluation measures, the coefficient of correlation ( R), Nash-Sutcliffe efficiency coefficient ( E), root mean squared error (RMSE), mean absolute percentage error (MAPE), are employed to evaluate the performances of various models developed. Two case study river sites are also provided to illustrate their respective performances. The results indicate that the best performance can be obtained by ANFIS, GP and SVM, in terms of different evaluation criteria during the training and validation phases.

  12. Capacity adequacy in power markets facing energy transition: A comparison of scarcity pricing and capacity mechanism

    International Nuclear Information System (INIS)

    Petitet, Marie; Finon, Dominique; Janssen, Tanguy

    2017-01-01

    This article analyses how a capacity mechanism can address security of supply objectives in a power market undergoing an energy transition that combines energy efficiency efforts to stabilise demand and a rapid increase in the proportion of renewables. To analyse this situation, power markets are simulated over the long term with a System Dynamics model integrating new investment and closure decisions. This last trait is relevant to studying investment in power generation in mature markets undergoing policy shocks. The energy-only market design with a price cap, with and without a capacity mechanism, is compared to scarcity pricing in two investment behaviour scenarios with and without risk aversion. The results show that the three market designs lead to different levels of risk for peaking unit investment and results thus differ according to which risk aversion hypothesis is adopted. Assuming a risk-neutral investor, the results indicate that compared to an energy-only market with a price cap at 3 000 €/MWh, an energy-only market with scarcity pricing and the market design with a capacity mechanism are two efficient options to reach similar levels of load loss. But under the hypothesis of risk aversion, the results highlight the advantage of the capacity mechanism over scarcity pricing. - Highlights: • Investment decisions in electricity markets are simulated by a System Dynamics model. • Capacity mechanism enhances capacity adequacy compared to the energy-only market. • With no risk aversion, capacity mechanism or scarcity pricing provide similar results. • With risk aversion, capacity mechanism appears to be the preferable market design.

  13. Activity-based costing for pathology examinations and comparison with the current pricing system in Turkey.

    Science.gov (United States)

    Ergün, Ferda A K; Ağirbaş, Ismail; Kuzu, Işınsu

    2013-01-01

    To demonstrate the real cost data of the pathology examinations by using the activity-based costing method and to contribute to the financial planning of the departments, health managers and also the social security institution. Forty-four examinations selected from the Healthcare Implementation Notification system list and performed at the Ankara University Faculty of Medicine Pathology Department during September 2010 were studied. The analysis and the real cost calculations were done according to the duration of the procedures. Calculated costs were compared with the Healthcare Implementation Notification system and Medicare price lists. The costs of the pathology tests listed within the same pricing levels in the Healthcare Implementation Notification system list showed great differences. The minimum and maximum costs in level 1, 2, 3, and 4 were 15,98-80,15 TL, 15,95-258,59 TL, 42,38- 236,87 TL, and 124,42-406,76 TL, respectively. Medicare price levels were more consistent with the real costs of the examinations compared to the Healthcare Implementation Notification system price list. The prices of the pathology examination listed at different levels in the Healthcare Implementation Notification system lists do not cover the real costs of the work done. The principal parameters of Activity-Based Costing system are more suitable for making the most realistic cost categorization. Although the prices could differ between countries, the Medicare system categories are more realistic than the Healthcare Implementation Notification system. The Healthcare Implementation Notification system list needs to be revised in order to reflect the real costs of the pathology examinations.

  14. A comparison of discounted cashflow and modern asset pricing methods - project selection and policy implications

    International Nuclear Information System (INIS)

    Emhjellen, Magne; Alaouze, Chris M.

    2003-01-01

    We examine the differences in the net present values (NPVs) of North Sea oil projects obtained using the weighted average cost of capital and a modern asset pricing (MAP) method which involves the separate discounting of project cashflow components. NPV differences of more than $10 million were found for some oil projects. Thus, the choice of valuation method will affect the development decisions of oil companies and could influence tax policy. The results of the MAP method are very sensitive to the choice of parameter values for the stochastic process used to model oil prices. Further research is recommended before the MAP method is used as the sole valuation model

  15. Models of Investor Forecasting Behavior — Experimental Evidence

    Directory of Open Access Journals (Sweden)

    Federico Bonetto

    2017-12-01

    Full Text Available Different forecasting behaviors affect investors’ trading decisions and lead to qualitatively different asset price trajectories. It has been shown in the literature that the weights that investors place on observed asset price changes when forecasting future price changes, and the nature of their confidence when price changes are forecast, determine whether price bubbles, price crashes, and unpredictable price cycles occur. In this paper, we report the results of behavioral experiments involving multiple investors who participated in a market for a virtual asset. Our goal is to study investors’ forecast formation. We conducted three experimental sessions with different participants in each session. We fit different models of forecast formation to the observed data. There is strong evidence that the investors forecast future prices by extrapolating past price changes, even when they know the fundamental value of the asset exactly and the extrapolated forecasts differ significantly from the fundamental value. The rational expectations hypothesis seems inconsistent with the observed forecasts. The forecasting models of all participants that best fit the observed forecasting data were of the type that cause price bubbles and cycles in dynamical systems models, and price bubbles and cycles ended up occurring in all three sessions.

  16. A Price Comparison of Antibiotics in a Tertiary Healthcare Facility in ...

    African Journals Online (AJOL)

    A large proportion of the population in developing countries including Nigeria lacks regular access to medicines for disease treatment. This has been linked to the high cost of these drugs, antibiotics inclusive. We undertook to compare the prices of antibiotics prescribed in the pediatric ward of a tertiary health-care facility in ...

  17. Transport fuel demand responses to fuel price and income projections : Comparison of integrated assessment models

    NARCIS (Netherlands)

    Edelenbosch, O. Y.; van Vuuren, Detlef; Bertram, C.; Carrara, S.; Emmerling, J.; Daly, H.; Kitous, A.; McCollum, D. L.; Saadi Failali, N.

    Income and fuel price pathways are key determinants in projections of the energy system in integrated assessment models. In recent years, more details have been added to the transport sector representation in these models. To better understand the model dynamics, this manuscript analyses transport

  18. A Comparison Study of Return Ratio-Based Academic Enrollment Forecasting Models. Professional File. Article 129, Spring 2013

    Science.gov (United States)

    Zan, Xinxing Anna; Yoon, Sang Won; Khasawneh, Mohammad; Srihari, Krishnaswami

    2013-01-01

    In an effort to develop a low-cost and user-friendly forecasting model to minimize forecasting error, we have applied average and exponentially weighted return ratios to project undergraduate student enrollment. We tested the proposed forecasting models with different sets of historical enrollment data, such as university-, school-, and…

  19. Long-term forecast 2010; Laangsiktsprognos 2010

    Energy Technology Data Exchange (ETDEWEB)

    2011-07-01

    This report presents the energy forecast to the year 2030, and two different sensitivity scenarios. The forecast is based on existing instruments, which means that the report's findings should not be considered a proper forecast of the future energy use, but as an impact assessment of existing policy instruments, given different circumstances such as economic growth and fuel prices

  20. Natural gas pricing and contracting practices in North America

    International Nuclear Information System (INIS)

    Hassan, F.

    1992-01-01

    Over the past 5 years the natural gas industry in North America has undergone substantial change as a result of the deregulated market. A comparison is provided of the key contract parameters in gas purchase contracts utilized by local distribution companies, industrial customers, cogenerators and marketers. Issues discussed include pricing mechanisms, indexed contracts, negotiated contracts, combinations, dispute resolution, supply, government regulation, industry structures, financial considerations, perception, geological influences, demand, transmission, storage, distribution, price trends and forecasts, Order 636 in the U.S., the evolution of North American market hubs, the futures market, and 'daisy chains' of connecting pipelines. 15 refs., 7 figs., 1 tab

  1. Petroleum price; Prix du petrole

    Energy Technology Data Exchange (ETDEWEB)

    Maurice, J

    2001-07-01

    The oil market is the most volatile of all markets, with the exception of the Nasdaq. It is also the biggest commodity market in the world. Therefore one cannot avoid forecasting oil prices, nor can one expect to avoid the forecasting errors that have been made in the past. In his report, Joel Maurice draws a distinction between the short term and the medium-long term in analysing the outlook for oil prices. (author)

  2. Forecasting elections with mere recognition from small, lousy samples: A comparison of collective recognition, wisdom of crowds, and representative polls

    Directory of Open Access Journals (Sweden)

    Wolfgang Gaissmeier

    2011-02-01

    Full Text Available We investigated the extent to which the human capacity for recognition helps to forecast political elections: We compared naive recognition-based election forecasts computed from convenience samples of citizens' recognition of party names to (i standard polling forecasts computed from representative samples of citizens' voting intentions, and to (ii simple---and typically very accurate---wisdom-of-crowds-forecasts computed from the same convenience samples of citizens' aggregated hunches about election results. Results from four major German elections show that mere recognition of party names forecast the parties' electoral success fairly well. Recognition-based forecasts were most competitive with the other models when forecasting the smaller parties' success and for small sample sizes. However, wisdom-of-crowds-forecasts outperformed recognition-based forecasts in most cases. It seems that wisdom-of-crowds-forecasts are able to draw on the benefits of recognition while at the same time avoiding its downsides, such as lack of discrimination among very famous parties or recognition caused by factors unrelated to electoral success. Yet it seems that a simple extension of the recognition-based forecasts---asking people what proportion of the population would recognize a party instead of whether they themselves recognize it---is also able to eliminate these downsides.

  3. Comparison of random forests and support vector machine for real-time radar-derived rainfall forecasting

    Science.gov (United States)

    Yu, Pao-Shan; Yang, Tao-Chang; Chen, Szu-Yin; Kuo, Chen-Min; Tseng, Hung-Wei

    2017-09-01

    This study aims to compare two machine learning techniques, random forests (RF) and support vector machine (SVM), for real-time radar-derived rainfall forecasting. The real-time radar-derived rainfall forecasting models use the present grid-based radar-derived rainfall as the output variable and use antecedent grid-based radar-derived rainfall, grid position (longitude and latitude) and elevation as the input variables to forecast 1- to 3-h ahead rainfalls for all grids in a catchment. Grid-based radar-derived rainfalls of six typhoon events during 2012-2015 in three reservoir catchments of Taiwan are collected for model training and verifying. Two kinds of forecasting models are constructed and compared, which are single-mode forecasting model (SMFM) and multiple-mode forecasting model (MMFM) based on RF and SVM. The SMFM uses the same model for 1- to 3-h ahead rainfall forecasting; the MMFM uses three different models for 1- to 3-h ahead forecasting. According to forecasting performances, it reveals that the SMFMs give better performances than MMFMs and both SVM-based and RF-based SMFMs show satisfactory performances for 1-h ahead forecasting. However, for 2- and 3-h ahead forecasting, it is found that the RF-based SMFM underestimates the observed radar-derived rainfalls in most cases and the SVM-based SMFM can give better performances than RF-based SMFM.

  4. Comparison of Solar and Wind Power Output and Correlation with Real-Time Pricing

    Science.gov (United States)

    Hoepfl, Kathryn E.; Compaan, Alvin D.; Solocha, Andrew

    2011-03-01

    This study presents a method that can be used to determine the least volatile power output of a wind and solar hybrid energy system in which wind and solar systems have the same peak power. Hourly data for wind and PV systems in Northwest Ohio are used to show that a combination of both types of sustainable energy sources produces a more stable power output and would be more valuable to the grid than either individually. This method could be used to determine the ideal ratio in any part of the country and should help convince electric utility companies to bring more renewable generation online. This study also looks at real-time market pricing and how each system (solar, wind, and hybrid) correlates with 2009 hourly pricing from the Midwest Interconnect. KEH acknowledges support from the NSF-REU grant PHY-1004649 to the Univ. of Toledo and Garland Energy Systems/Ohio Department of Development.

  5. Use of comparative effectiveness research in drug coverage and pricing decisions: a six-country comparison.

    Science.gov (United States)

    Sorenson, Corinna

    2010-07-01

    Comparative effectiveness research (CER) has assumed an increasing role in drug coverage and, in some cases, pricing decisions in Europe, as decision-makers seek to obtain better value for money. This issue brief comparatively examines the use of CER across six countries--Denmark, England, France, Germany, the Netherlands, and Sweden. With CER gaining traction in the United States, these international experiences offer insights and potential lessons. Investing in CER can help address the current gap in publicly available, credible, up-to-date, and scientifically based comparative information on the effectiveness of drugs and other health interventions. This information can be used to base coverage and pricing decisions on evidence of value, thereby facilitating access to and public and private investment in the most beneficial new drugs and technologies. In turn, use of CER creates incentives for more efficient, high-quality health care and encourages development of innovative products that offer measurable value to patients.

  6. A comparison of alternative medicare reimbursement policies under optimal hospital pricing.

    Science.gov (United States)

    Dittman, D A; Morey, R C

    1983-01-01

    This paper applies and extends the use of a nonlinear hospital pricing model, recently posited in the literature by Dittman and Morey [1]. That model applied a hospital profit-maximizing behavior and studied the effects of optimal pricing of hospital ancillary services on the incidence of payment by private insurance companies and the Medicare trust fund. Here, we examine variations of the above model where both hospital profit-maximizing and profit-satisficing postures are of interest. We apply the model to three types of Medicare reimbursement policies currently in use or under legislative mandate to implement. The policies differ according to hospital size and whether cross-subsidies are allowed. We are interested in determining the effects of profit-maximizing and -satisficing behaviors of these three reimbursement policies on the levels of profits received, and on the respective implications for private payors and the Medicare trust fund. PMID:6347973

  7. Preferential Price and Trade Tied Aid in Fiji: Implications on Price ...

    African Journals Online (AJOL)

    Pacific island countries (PICs) have been receiving the highest percapita aid .... dimension of the preferential price by examining the role of forecast price as an ..... Abbot, D and S. Pollard (2004) Hardship and Poverty in the Pacific, Asian.

  8. A comparison of LOD and UT1-UTC forecasts by different combined prediction techniques

    Science.gov (United States)

    Kosek, W.; Kalarus, M.; Johnson, T. J.; Wooden, W. H.; McCarthy, D. D.; Popiński, W.

    Stochastic prediction techniques including autocovariance, autoregressive, autoregressive moving average, and neural networks were applied to the UT1-UTC and Length of Day (LOD) International Earth Rotation and Reference Systems Servive (IERS) EOPC04 time series to evaluate the capabilities of each method. All known effects such as leap seconds and solid Earth zonal tides were first removed from the observed values of UT1-UTC and LOD. Two combination procedures were applied to predict the resulting LODR time series: 1) the combination of the least-squares (LS) extrapolation with a stochastic predition method, and 2) the combination of the discrete wavelet transform (DWT) filtering and a stochastic prediction method. The results of the combination of the LS extrapolation with different stochastic prediction techniques were compared with the results of the UT1-UTC prediction method currently used by the IERS Rapid Service/Prediction Centre (RS/PC). It was found that the prediction accuracy depends on the starting prediction epochs, and for the combined forecast methods, the mean prediction errors for 1 to about 70 days in the future are of the same order as those of the method used by the IERS RS/PC.

  9. A method for the correction of the feed-in tariff price for cogeneration based on a comparison between Croatia and EU Member States

    International Nuclear Information System (INIS)

    Uran, Vedran; Krajcar, Slavko

    2009-01-01

    The European Commission has adopted Directive 2004/8/EC on the promotion of cogeneration, which the EU Member States, as well as candidates including Croatia, were obliged to accept. Among other terms and conditions, the Directive requires certain support mechanisms, such as feed-in tariff prices and premiums added to market electricity prices. In this paper, the cost effectiveness of selling electricity at the feed-in tariff prices in the selected EU Member States is compared to selling it on the European electricity market, with or without premiums. The results of this comparison will indicate whether correction of the Croatian feed-in tariff price to a higher value would be justified. The cost effectiveness ratio of a cogeneration unit upgraded with mean reverting and jump diffusion processes is used for comparison. At the end of this paper, a method is suggested for the correction of feed-in tariff prices, with examples of corrected prices for the years 2008 and 2009. Such corrections have been proven to be justified and are compared to the feed-in tariff prices in most of the selected EU Member States.

  10. The outlook for oil prices in 1992 - results of a survey

    International Nuclear Information System (INIS)

    Hawdon, D.

    1992-01-01

    The eighth in a series of oil price expectation studies took place on 18th March 1992 at the Prospects for Oil Prices conference held at the University of Surrey. Thirty-one participants returned a questionnaire designed to elicit 12 month ahead and 5 year ahead price expectations. Respondents were asked to indicate their view of the likely price of oil in certain broad price ranges. These were selected to cover the wide variation of prices experienced since the early 1970s. The results show the 12 month's ahead expectations all clustered in the range $10 to $25 per barrel and $16-$20 as the median predicted price. In comparison with the 1991 expectations, a much higher proportion of respondents (77.4 as compared to 50% in 1991) gave $16-20 as their expected price range, whilst fewer expected prices to rise (19% compared with 46% in 1991). The stability of the 12 month ahead price expectations is a remarkable feature of a period which has witnessed much tension in the Middle East and in the former Soviet Union. This stability extends to the 5 year ahead forecasts as well. Here the median expectation is for prices to rise to the $21-25 per barrel range in money of the day terms though there is evidence of a growing scepticism about the oil market's ability to sustain higher prices in the long run. (author)

  11. Tüketici Fiyat Endeksinin Uyarlamalı Ağa Dayalı Bulanık Çıkarım Sistemi ile Kestirimi / Consumer Price Index Forecast with Adaptive Neuro Fuzzy Inference System

    Directory of Open Access Journals (Sweden)

    Serenay VAROL

    2016-04-01

    Full Text Available Son yıllarda zaman serisi tahmini için birçok alternatif yöntem önerilmiştir. Uyarlamalı ağa dayalı bulanık çıkarım sistemi (ANFIS öngörü problemi için literatürde en çok uygulanan bulanık çıkarım sistemidir. Bu çalışmada tüketici fiyat endeksinin kestiriminde ANFIS’in performansı incelenmiştir. Çalışmanın sonucunda ANFIS yöntemi ile ilgilenilen zaman aralığındaki tüketici fiyat endeksinin kestiriminde ulaşılan sonuçlar yorumlanmıştır. / Alternative methods have been proposed for time series prediction in last years. Adaptive neuro fuzzy inference system (ANFIS is the most used fuzzy inference system in literature for prediction problem. In this study, the performance of ANFIS in forecasting consumer price index is examined, and the results of the consumer price index estimation in time period, on which ANFIS method is applied, are interpreted.

  12. Constructing Quality Adjusted Price Indexes: a Comparison of Hedonic and Discrete Choice Models

    OpenAIRE

    N. Jonker

    2001-01-01

    The Boskin report (1996) concluded that the US consumer price index (CPI) overestimated the inflation by 1.1 percentage points. This was due to several measurement errors in the CPI. One of them is called quality change bias. In this paper two methods are compared which can be used to eliminate quality change bias, namely the hedonic method and a method based on the use of discrete choice models. The underlying micro-economic fundations of the two methods are compared as well as their empiric...

  13. Understanding international commodity price fluctuations

    NARCIS (Netherlands)

    Arezki, Rabah; Loungani, Prakash; van der Ploeg, Rick; Venables, Anthony J.

    An overview is provided of recent work on commodity prices, focusing on three themes: (i) "financialization" of commodity markets--commodities being considered by financial investors as a distinct asset class, (ii) trends and forecasts of commodity prices, and (iii) fracking-a shorthand for the

  14. A Comparison of the Performance of Advanced Statistical Techniques for the Refinement of Day-ahead and Longer NWP-based Wind Power Forecasts

    Science.gov (United States)

    Zack, J. W.

    2015-12-01

    Predictions from Numerical Weather Prediction (NWP) models are the foundation for wind power forecasts for day-ahead and longer forecast horizons. The NWP models directly produce three-dimensional wind forecasts on their respective computational grids. These can be interpolated to the location and time of interest. However, these direct predictions typically contain significant systematic errors ("biases"). This is due to a variety of factors including the limited space-time resolution of the NWP models and shortcomings in the model's representation of physical processes. It has become common practice to attempt to improve the raw NWP forecasts by statistically adjusting them through a procedure that is widely known as Model Output Statistics (MOS). The challenge is to identify complex patterns of systematic errors and then use this knowledge to adjust the NWP predictions. The MOS-based improvements are the basis for much of the value added by commercial wind power forecast providers. There are an enormous number of statistical approaches that can be used to generate the MOS adjustments to the raw NWP forecasts. In order to obtain insight into the potential value of some of the newer and more sophisticated statistical techniques often referred to as "machine learning methods" a MOS-method comparison experiment has been performed for wind power generation facilities in 6 wind resource areas of California. The underlying NWP models that provided the raw forecasts were the two primary operational models of the US National Weather Service: the GFS and NAM models. The focus was on 1- and 2-day ahead forecasts of the hourly wind-based generation. The statistical methods evaluated included: (1) screening multiple linear regression, which served as a baseline method, (2) artificial neural networks, (3) a decision-tree approach called random forests, (4) gradient boosted regression based upon an decision-tree algorithm, (5) support vector regression and (6) analog ensemble

  15. Comparison of acoustic doppler current profiler and Price AA mechanical current meter measurements made during the 2011 Mississippi River Flood

    Science.gov (United States)

    O'Brien, Patrick; Mueller, David; Pratt, Thad

    2012-01-01

    The Mississippi River and Tributaries project performed as designed during the historic 2011 Mississippi River flood, with many of the operational decisions based on discharge targets as opposed to stage. Measurement of discharge at the Tarbert Landing, Mississippi range provides critical information used in operational decisions for the floodways located in Louisiana. Historically, discharge measurements have been made using a Price AA current meter and the mid-section method, and a long record exists based on these types of measurements, including historical peak discharges. Discharge measurements made using an acoustic Doppler current profiler from a moving boat have been incorporated into the record since the mid 1990's, and are used along with the Price AA mid-section measurements. During the 2011 flood event, both methods were used and appeared to provide different results at times. The apparent differences between the measurement techniques are due to complex hydrodynamics at this location that created large spatial and temporal fluctuations in the flow. The data and analysis presented herein show the difference between the two methods to be within the expected accuracy of the measurements when the measurements are made concurrently. The observed fluctuations prevent valid comparisons of data collected sequentially or even with different observation durations.

  16. An empirical examination of restructured electricity prices

    International Nuclear Information System (INIS)

    Knittel, C.R.; Roberts, M.R.

    2005-01-01

    We present an empirical analysis of restructured electricity prices. We study the distributional and temporal properties of the price process in a non-parametric framework, after which we parametrically model the price process using several common asset price specifications from the asset-pricing literature, as well as several less conventional models motivated by the peculiarities of electricity prices. The findings reveal several characteristics unique to electricity prices including several deterministic components of the price series at different frequencies. An 'inverse leverage effect' is also found, where positive shocks to the price series result in larger increases in volatility than negative shocks. We find that forecasting performance in dramatically improved when we incorporate features of electricity prices not commonly modelled in other asset prices. Our findings have implications for how empiricists model electricity prices, as well as how theorists specify models of energy pricing. (author)

  17. THE CORN-EGG PRICE TRANSMISSION MECHANISM

    OpenAIRE

    Babula, Ronald A.; Bessler, David A.

    1990-01-01

    A vector autoregression (VAR) model of corn, farm egg, and retail egg prices is estimated and shocked with a corn price increase. Impulse responses in egg prices, t-statistics for the impulse responses, and decompositions of forecast error variance are presented. Analyses of results provide insights on the corn/egg price transmission mechanism and on how corn price shocks pulsate through the egg-related economy.

  18. World coal prices and future energy demand

    International Nuclear Information System (INIS)

    Bennett, J.

    1992-01-01

    The Clean Air Act Amendments will create some important changes in the US domestic steam coal market, including price increases for compliance coal by the year 2000 and price decreases for high-sulfur coal. In the international market, there is likely to be a continuing oversupply which will put a damper on price increases. The paper examines several forecasts for domestic and international coal prices and notes a range of predictions for future oil prices

  19. Earthquake Forecasting Methodology Catalogue - A collection and comparison of the state-of-the-art in earthquake forecasting and prediction methodologies

    Science.gov (United States)

    Schaefer, Andreas; Daniell, James; Wenzel, Friedemann

    2015-04-01

    Earthquake forecasting and prediction has been one of the key struggles of modern geosciences for the last few decades. A large number of approaches for various time periods have been developed for different locations around the world. A categorization and review of more than 20 of new and old methods was undertaken to develop a state-of-the-art catalogue in forecasting algorithms and methodologies. The different methods have been categorised into time-independent, time-dependent and hybrid methods, from which the last group represents methods where additional data than just historical earthquake statistics have been used. It is necessary to categorize in such a way between pure statistical approaches where historical earthquake data represents the only direct data source and also between algorithms which incorporate further information e.g. spatial data of fault distributions or which incorporate physical models like static triggering to indicate future earthquakes. Furthermore, the location of application has been taken into account to identify methods which can be applied e.g. in active tectonic regions like California or in less active continental regions. In general, most of the methods cover well-known high-seismicity regions like Italy, Japan or California. Many more elements have been reviewed, including the application of established theories and methods e.g. for the determination of the completeness magnitude or whether the modified Omori law was used or not. Target temporal scales are identified as well as the publication history. All these different aspects have been reviewed and catalogued to provide an easy-to-use tool for the development of earthquake forecasting algorithms and to get an overview in the state-of-the-art.

  20. Forecast Combinations

    OpenAIRE

    Timmermann, Allan G

    2005-01-01

    Forecast combinations have frequently been found in empirical studies to produce better forecasts on average than methods based on the ex-ante best individual forecasting model. Moreover, simple combinations that ignore correlations between forecast errors often dominate more refined combination schemes aimed at estimating the theoretically optimal combination weights. In this paper we analyse theoretically the factors that determine the advantages from combining forecasts (for example, the d...

  1. Forecast combinations

    OpenAIRE

    Aiolfi, Marco; Capistrán, Carlos; Timmermann, Allan

    2010-01-01

    We consider combinations of subjective survey forecasts and model-based forecasts from linear and non-linear univariate specifications as well as multivariate factor-augmented models. Empirical results suggest that a simple equal-weighted average of survey forecasts outperform the best model-based forecasts for a majority of macroeconomic variables and forecast horizons. Additional improvements can in some cases be gained by using a simple equal-weighted average of survey and model-based fore...

  2. Draft forecast of the final report for the comparison to 40 CFR Part 191, Subpart B, for the Waste Isolation Pilot Plant

    Energy Technology Data Exchange (ETDEWEB)

    Bertram-Howery, S.G.; Marietta, M.G.; Anderson, D.R.; Gomez, L.S.; Rechard, R.P. (Sandia National Labs., Albuquerque, NM (USA)); Brinster, K.F.; Guzowski, R.V. (Science Applications International Corp., Albuquerque, NM (USA))

    1989-12-01

    The United States Department of Energy is planning to dispose of transuranic wastes, which have been generated by defense programs, at the Waste Isolation Pilot Plant. The WIPP Project will assess compliance with the requirements of the United States Environmental Protection Agency. This report forecasts the planned 1992 document, Comparison to 40 CFR, Part 191, Subpart B, for the Waste Isolation Pilot Plant (WIPP). 130 refs., 36 figs., 11 tabs.

  3. Forecasting Exchange Rates and Relative Prices with the Hamburger Standard: Is What You Want What You Get With McParity?

    OpenAIRE

    Robert E. Cumby

    1996-01-01

    A decade ago the Economist began an annual survey of Big Mac prices as a guide to whether currencies are trading at the right exchange rates. This paper asks how well the hamburger standard has performed. Although average deviations from absolute Big Mac parity are large for several currencies, once estimates of these average deviations are removed from the data, the evidence suggests that convergence to relative Big Mac parity is quite rapid. The half-life of deviations from Big Mac parity a...

  4. Trends in the forecast of the world prices for selected metals and their influence on the exploitation of the Slovak raw mineral base

    Directory of Open Access Journals (Sweden)

    Slavkovský Jozef

    2000-06-01

    Full Text Available In this paper is given a basic information about the situation in the ore raw material base of the Slovak republic, after its transition to the market economy in the years 1990 – 1994. By dumping the ore mining, a decrease in the ore production, especially their sortiment, also started. Therefore only two ore mines – Nižná Slaná (Fe ores and Banská Hodruša (Au ores are in operation in Slovakia at present time. The rest of Slovak ores are economically not viable after present criteria. Besides the evaluation of balanced and unbalanced ore deposits, and the deposit´s parameters, the knowledge about trends of world ores and metal prices are very important. From this point of view, ores and metals which have a great importance (Fe, Al, Cu, Sb, Ag, Au for Slovakia are discussed. The obtained results have a prognostic character and they should be considered at the utilisation of own mineral base, as well as when buying mineral raw materials from abroad. In both cases the information about trends of world prices of raw materials play an important role.

  5. Supplement to the Annual Energy Outlook 1993

    International Nuclear Information System (INIS)

    1993-01-01

    The Supplement to the Annual Energy Outlook 1993 is a companion document to the Energy Information Administration's (EIA) Annual Energy Outlook 1993 (AEO). Supplement tables provide the regional projections underlying the national data and projections in the AEO. The domestic coal, electric power, commercial nuclear power, end-use consumption, and end-use price tables present AEO forecasts at the 10 Federal Region level. World coal tables provide data and projections on international flows of steam coal and metallurgical coal, and the oil and gas tables provide the AEO oil and gas supply forecasts by Oil and Gas Supply Regions and by source of supply. All tables refer to cases presented in the AEO, which provides a range of projections for energy markets through 2010

  6. The pricing behavior comparison of Canada and Australia exporter in wheat international market using Pricing to Market (PTM) and Residual Demand Elasticity (RDE)

    Science.gov (United States)

    Wibowo, R. P.; Sumono; Iddrisu, Y.; Darus, M.; Sihombing, L. P.; Jufri

    2018-02-01

    This paper try to identify and examined the degree of market power on wheat international market by 2 major exporting countries comprising Canada and Australia by using the Pricing to Market (PTM) method and Residual Demand Elasticity (RDE) method. The PTM method found that Canada impose noncompetitive strategy by applying price discrimination and apply market power to their importing. Different results come from Australian exporter as they are not using their market power to the importing. Conflicting result arise from estimation using RDE and PTM method suggest that the need to extend the theoretical model of both model by expand its economic and econometric model to have consistent expected result theoretically and empirically.

  7. Long-term projections for electricity and gas prices

    International Nuclear Information System (INIS)

    Borggrefe, Frieder; Lochner, Stefan

    2009-01-01

    The article analyses potential developments of wholesale electricity prices in Germany until 2030. The relevant determinants and their effects on prices are shown. Several projections demonstrate the impact of future fuel prices taking the political framework into account. The importance of carbon and gas prices - and the latter's relationship to oil prices - are discussed extensively. Although forecasting electricity prices is associated with great uncertainties, the article illustrates the relative impacts of the various price determinants and their interactions. (orig.)

  8. Value-based pricing

    Directory of Open Access Journals (Sweden)

    Netseva-Porcheva Tatyana

    2010-01-01

    Full Text Available The main aim of the paper is to present the value-based pricing. Therefore, the comparison between two approaches of pricing is made - cost-based pricing and value-based pricing. The 'Price sensitively meter' is presented. The other topic of the paper is the perceived value - meaning of the perceived value, the components of perceived value, the determination of perceived value and the increasing of perceived value. In addition, the best company strategies in matrix 'value-cost' are outlined. .

  9. Comparison of integrated assessment models: Carbon price impacts on U.S. energy

    International Nuclear Information System (INIS)

    Wilkerson, Jordan T.; Leibowicz, Benjamin D.; Turner, Delavane D.; Weyant, John P.

    2015-01-01

    Integrated assessment models (IAMs) are increasingly used to evaluate carbon policy impacts on energy structure, but different models can yield considerably different results. This paper seeks to frame model results for policymakers and other consumers of model outputs. In this analysis we compare three models: GCAM, MERGE, and EPPA. We apply diagnostic carbon price scenarios, such as ramps and shocks, to identify key differences in model behavior that drive inter-model variability in projected policy impacts on the U.S. energy system. We report model results using several economic parameterizations and find that variation in carbon emissions across models results primarily from differences in carbon intensity of energy supply. These differences arise because models include different low-carbon energy technology options and vary widely in how flexible the electricity supply sector is at adapting to a change in policy. The timing of emissions abatement is also strongly influenced by whether the model is a simulation or an inter-temporal optimization scheme and the amount of foresight exhibited in the model. Our analysis demonstrates the usefulness of novel IAM diagnostic indicators and clarifies model features that are highly relevant for consumers of model results. - Highlights: • We compare several IAMs and highlight differences between models types. • Structural difference between models leads to significantly different energy mix. • Primary driver of inter-model variability is changes in carbon intensity. • Intensity driven by technology options, constraints on adoption, and substitutability. • Policymakers need to understand model framework, inputs, and assumptions

  10. Estimating predictive hydrological uncertainty by dressing deterministic and ensemble forecasts; a comparison, with application to Meuse and Rhine

    Science.gov (United States)

    Verkade, J. S.; Brown, J. D.; Davids, F.; Reggiani, P.; Weerts, A. H.

    2017-12-01

    Two statistical post-processing approaches for estimation of predictive hydrological uncertainty are compared: (i) 'dressing' of a deterministic forecast by adding a single, combined estimate of both hydrological and meteorological uncertainty and (ii) 'dressing' of an ensemble streamflow forecast by adding an estimate of hydrological uncertainty to each individual streamflow ensemble member. Both approaches aim to produce an estimate of the 'total uncertainty' that captures both the meteorological and hydrological uncertainties. They differ in the degree to which they make use of statistical post-processing techniques. In the 'lumped' approach, both sources of uncertainty are lumped by post-processing deterministic forecasts using their verifying observations. In the 'source-specific' approach, the meteorological uncertainties are estimated by an ensemble of weather forecasts. These ensemble members are routed through a hydrological model and a realization of the probability distribution of hydrological uncertainties (only) is then added to each ensemble member to arrive at an estimate of the total uncertainty. The techniques are applied to one location in the Meuse basin and three locations in the Rhine basin. Resulting forecasts are assessed for their reliability and sharpness, as well as compared in terms of multiple verification scores including the relative mean error, Brier Skill Score, Mean Continuous Ranked Probability Skill Score, Relative Operating Characteristic Score and Relative Economic Value. The dressed deterministic forecasts are generally more reliable than the dressed ensemble forecasts, but the latter are sharper. On balance, however, they show similar quality across a range of verification metrics, with the dressed ensembles coming out slightly better. Some additional analyses are suggested. Notably, these include statistical post-processing of the meteorological forecasts in order to increase their reliability, thus increasing the reliability

  11. Selecting the Best Forecasting-Implied Volatility Model Using Genetic Programming

    Directory of Open Access Journals (Sweden)

    Wafa Abdelmalek

    2009-01-01

    Full Text Available The volatility is a crucial variable in option pricing and hedging strategies. The aim of this paper is to provide some initial evidence of the empirical relevance of genetic programming to volatility's forecasting. By using real data from S&P500 index options, the genetic programming's ability to forecast Black and Scholes-implied volatility is compared between time series samples and moneyness-time to maturity classes. Total and out-of-sample mean squared errors are used as forecasting's performance measures. Comparisons reveal that the time series model seems to be more accurate in forecasting-implied volatility than moneyness time to maturity models. Overall, results are strongly encouraging and suggest that the genetic programming approach works well in solving financial problems.

  12. On the directional accuracy of survey forecasts: the case of gold and silver

    DEFF Research Database (Denmark)

    Fritsche, U.; Pierdzioch, C.; Rulke, J. C.

    2013-01-01

    We use a nonparametric market-timing test to study the directional accuracy of survey forecasts of the prices of gold and silver. We find that forecasters have market-timing ability with respect to the direction of change of the price of silver at various forecast horizons. In contrast, forecasters...... have no market-timing ability with respect to the direction of change in the gold price. Combining forecasts of both metal prices to set up a multivariate market-timing test yields no evidence of joint predictability of the directions of change of the prices of gold and silver....

  13. A Note on Price Risks in Swiss Crop Production – Empirical Results and Comparisons with other Countries

    NARCIS (Netherlands)

    Finger, R.; Benni, El N.

    2012-01-01

    The liberalization of Swiss agricultural markets will not only decrease crop price levels but is also expected to increase the volatility of prices. Even though these potential increases in price volatilities for Swiss producers are acknowledged as an important fact, no empirical estimates are

  14. Supplement to the annual energy outlook 1994

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1994-03-01

    This report is a companion document to the Annual Energy Outlook 1994 (AEO94), (DOE/EIA-0383(94)), released in Jan. 1994. Part I of the Supplement presents the key quantitative assumptions underlying the AEO94 projections, responding to requests by energy analysts for additional information on the forecasts. In Part II, the Supplement provides regional projections and other underlying details of the reference case projections in the AEO94. The AEO94 presents national forecasts of energy production, demand and prices through 2010 for five scenarios, including a reference case and four additional cases that assume higher and lower economic growth and higher and lower world oil prices. These forecasts are used by Federal, State, and local governments, trade associations, and other planners and decisionmakers in the public and private sectors.

  15. Forecasting oilfield economic performance

    International Nuclear Information System (INIS)

    Bradley, M.E.; Wood, A.R.O.

    1994-01-01

    This paper presents a general method for forecasting oilfield economic performance that integrates cost data with operational, reservoir, and financial information. Practices are developed for determining economic limits for an oil field and its components. The economic limits of marginal wells and the role of underground competition receive special attention. Also examined is the influence of oil prices on operating costs. Examples illustrate application of these concepts. Categorization of costs for historical tracking and projections is recommended

  16. Forecasting Skill

    Science.gov (United States)

    1981-01-01

    for the third and fourth day precipitation forecasts. A marked improvement was shown for the consensus 24 hour precipitation forecast, and small... Zuckerberg (1980) found a small long term skill increase in forecasts of heavy snow events for nine eastern cities. Other National Weather Service...and maximum temperature) are each awarded marks 2, 1, or 0 according to whether the forecast is correct, 8 - *- -**■*- ———"—- - -■ t0m 1 MM—IB I

  17. The accuracy comparison between ARFIMA and singular spectrum analysis for forecasting the sales volume of motorcycle in Indonesia

    Science.gov (United States)

    Sitohang, Yosep Oktavianus; Darmawan, Gumgum

    2017-08-01

    This research attempts to compare between two forecasting models in time series analysis for predicting the sales volume of motorcycle in Indonesia. The first forecasting model used in this paper is Autoregressive Fractionally Integrated Moving Average (ARFIMA). ARFIMA can handle non-stationary data and has a better performance than ARIMA in forecasting accuracy on long memory data. This is because the fractional difference parameter can explain correlation structure in data that has short memory, long memory, and even both structures simultaneously. The second forecasting model is Singular spectrum analysis (SSA). The advantage of the technique is that it is able to decompose time series data into the classic components i.e. trend, cyclical, seasonal and noise components. This makes the forecasting accuracy of this technique significantly better. Furthermore, SSA is a model-free technique, so it is likely to have a very wide range in its application. Selection of the best model is based on the value of the lowest MAPE. Based on the calculation, it is obtained the best model for ARFIMA is ARFIMA (3, d = 0, 63, 0) with MAPE value of 22.95 percent. For SSA with a window length of 53 and 4 group of reconstructed data, resulting MAPE value of 13.57 percent. Based on these results it is concluded that SSA produces better forecasting accuracy.

  18. Point and interval forecasts of mortality rates and life expectancy: A comparison of ten principal component methods

    Directory of Open Access Journals (Sweden)

    Han Lin Shang

    2011-07-01

    Full Text Available Using the age- and sex-specific data of 14 developed countries, we compare the point and interval forecast accuracy and bias of ten principal component methods for forecasting mortality rates and life expectancy. The ten methods are variants and extensions of the Lee-Carter method. Based on one-step forecast errors, the weighted Hyndman-Ullah method provides the most accurate point forecasts of mortality rates and the Lee-Miller method is the least biased. For the accuracy and bias of life expectancy, the weighted Hyndman-Ullah method performs the best for female mortality and the Lee-Miller method for male mortality. While all methods underestimate variability in mortality rates, the more complex Hyndman-Ullah methods are more accurate than the simpler methods. The weighted Hyndman-Ullah method provides the most accurate interval forecasts for mortality rates, while the robust Hyndman-Ullah method provides the best interval forecast accuracy for life expectancy.

  19. Air Pollution Forecasts: An Overview

    Science.gov (United States)

    Bai, Lu; Wang, Jianzhou; Lu, Haiyan

    2018-01-01

    Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies. PMID:29673227

  20. Air Pollution Forecasts: An Overview.

    Science.gov (United States)

    Bai, Lu; Wang, Jianzhou; Ma, Xuejiao; Lu, Haiyan

    2018-04-17

    Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies.

  1. Air Pollution Forecasts: An Overview

    Directory of Open Access Journals (Sweden)

    Lu Bai

    2018-04-01

    Full Text Available Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies.

  2. The mirage of higher petroleum prices

    International Nuclear Information System (INIS)

    Lynch, M.C.

    1996-01-01

    Most petroleum industry price forecasters do not possess a record of which they can be proud. Long-term petroleum market forecasting has been so inaccurate that it has often been described as virtually impossible. To avoid criticism of their performance, many organizations no longer circulate their forecasts. Why have the forecasts been so wrong? Because of failure to predict supply. This paper reviews the erroneous methods used to predict price trends in the oil and gas industry and identifies methods to correct the problem

  3. Estimating the common trend rate of inflation for consumer prices and consumer prices excluding food and energy prices

    OpenAIRE

    Michael T. Kiley

    2008-01-01

    I examine the common trend in inflation for consumer prices and consumer prices excluding prices of food and energy. Both the personal consumption expenditure (PCE) indexes and the consumer price indexes (CPI) are examined. The statistical model employed is a bivariate integrated moving average process; this model extends a univariate model that fits the data on inflation very well. The bivariate model forecasts as well as the univariate models. The results suggest that the relationship betwe...

  4. Energy forecasts, perspectives and methods

    Energy Technology Data Exchange (ETDEWEB)

    Svensson, J E; Mogren, A

    1984-01-01

    The authors have analyzed different methods for long term energy prognoses, in particular energy consumption forecasts. Energy supply and price prognoses are also treated, but in a less detailed manner. After defining and discussing the various methods/models used in forecasts, a generalized discussion of the influence on the prognoses from the perspectives (background factors, world view, norms, ideology) of the prognosis makers is given. Some basic formal demands that should be asked from any rational forecast are formulated and discussed. The authors conclude that different forecasting methodologies are supplementing each other. There is no best method, forecasts should be accepted as views of the future from differing perspectives. The primary prognostic problem is to show the possible futures, selecting the wanted future is a question of political process.

  5. The determinants of oil prices

    International Nuclear Information System (INIS)

    Angelier, J-P.

    1991-01-01

    In recent years, swings in oil prices have been of unprecendented severity and frequency. Three factors work together to determine the price of oil: in the short term, the balance between supply and demand; in the medium term, the structure of the oil industry; and in the long term, the marginal production cost consistent with world oil demand. An oil price forecast is presented based on these considerations, and it is predicted that in the year 2000, oil prices will not be significantly different from those of today. 28 refs

  6. Pricing transmission services

    International Nuclear Information System (INIS)

    Haaden, E.

    1995-01-01

    The price structure for transmission of electric power through the main lines in Sweden is analyzed. After deregulation of the electricity market, the main transmission lines are owned by a separate national company, with no interests from the power producers. Comparisons are made to ideal marginal price structures. 6 refs

  7. Inflow forecasting at BPA

    Energy Technology Data Exchange (ETDEWEB)

    McManamon, A. [Bonneville Power Administration, Portland, OR (United States)

    2007-07-01

    The Columbia River Power System operates with consideration for flood control, endangered species, navigation, irrigation, water supply, recreation, other fish and wildlife concerns and power production. The Bonneville Power Association (BPA) located in Portland, Oregon is responsible for 35-40 per cent of the power consumed within the region. This presentation discussed inflow power concerns at BPA. The presentation illustrated elevational relief of projects; annual and daily variability; the hydrologic cycle; national river service weather forecasting service (NRSWFS); components of NRSWFS; and hydrologic forecast locations. Project operations and inventory were included along with a comparison of the 71-year average unregulated flow with regulated flow at the Dalles. Consistency between short-term and long-term forecasts and long-term streamflow forecasts were also illustrated in graphical format. The presentation also discussed the issue of reducing model and parameter uncertainty; reducing initial conditions uncertainty; snow updating; and reducing meteorological uncertainty. tabs., figs.

  8. The impact of the internet on community pharmacy practice: a comparison of a Delphi panel's forecast with emerging trends.

    Science.gov (United States)

    Holmes, Erin R; Tipton, David J; Desselle, Shane P

    2002-01-01

    The purpose of this study was to forecast the impact of Internet pharmacy commerce on various stakeholders. A panel of experts assembled from a list of academicians in the social and administrative pharmaceutical sciences participated in a three-iteration Delphi procedure. Feedback from the aggregate responses of the panel was used to construct questionnaires employed in subsequent iterations of the Delphi. The panel converged to form a consensus upon a variety of issues. They forecasted the attainment of a 10-15% share in the market of prescription and over-the-counter pharmaceuticals through on-line purchases, the formation of strategic alliances among stakeholders in the drug distribution process, a shift in marketing strategies by brick-and-mortar stores, an increase in the prevalence of niching among pharmacy service providers and a subsequent growth in the implementation of cognitive services throughout the industry. With few exceptions, the forecast produced by the Delphi panel appears to be coming to fruition.

  9. Evaluating the US government's crude oil price projections

    International Nuclear Information System (INIS)

    Williams, M.D.

    1992-01-01

    The U.S. Department of Energy's (DOE) 1991 official long run crude oil price projections are evaluated by comparing parameter averages for the forecast period (1991-2010) to parameter averages from crude oil price history (1859-1990). The parameters used in the evaluation are average price, average annual price changes, and average cycle duration (in years). All prices used in the analysis are annual prices in constant 1990 dollars per barrel. 13 figs

  10. A comparison of boundary-layer heights inferred from wind-profiler backscatter profiles with diagnostic calculations using regional model forecasts

    Energy Technology Data Exchange (ETDEWEB)

    Baltink, H.K.; Holtslag, A.A.M. [Royal Netherlands Meteorological Inst., KNMI, De Bilt (Netherlands)

    1997-10-01

    From October 1994 through January 1997 the Tropospheric Energy Budget Experiment (TEBEX) was executed by KNMI. The main objectives are to study boundary layer processes and cloud variability on the sub-grid scale of present Global Climate Models and to improve the related sub-grid parametrizations. A suite of instruments was deployed to measure a large number of variables. Measurements to characterize ABL processes were focussed around the 200 m high meteorological observation tower of the KNMI in Cabauw. In the framework of TEBEX a 1290 MHz wind-profiler/RASS was installed in July 1994 at 300 m from tower. Data collected during TEBEX are used to assess the performance of a Regional Atmospheric Climate Model (RACMO). This climate model runs also in a operational forecast mode once a day. The diagnostic ABL-height (h{sub model}) is calculated from the RACMO forecast output. A modified Richardson`s number method extended with an excess parcel temperature is applied for all stability conditions. We present the preliminary results of a comparison of h{sub model} from forecasts with measured h{sub TS} derived from profiler and sodar data for July 1995. (au)

  11. Near-term oil prices

    International Nuclear Information System (INIS)

    Lynch, M.C.

    2001-01-01

    This PowerPoint presentation included 36 slides that described the state of oil prices and how to predict them. Prices are random, stochastic, chaotic, mean-reverting and driven by speculators, oil companies and OPEC. The many factors that enable price forecasting are economic growth, weather, industry behaviour, speculators, OPEC policy choices, Mexico/Russia production policy, non-OPEC supply and the interpretation of the above factors by OPEC, speculators, traders and the petroleum industry. Several graphs were included depicting such things as WTI price forecasts, differentials, oil market change in 2001, inventory levels, and WTI backwardation. The presentation provided some explanations for price uncertainties, price surges and collapses. U.S. GDP growth and the volatility of Iraq's production was also depicted. The author predicted that economic growth will occur and that oil demand will go up. Oil prices will fluctuate as the Middle East will be politically unstable and weather will be a major factor that will influence oil prices. The prices are likely to be more volatile than in the 1986 to 1995 period. 2 tabs., 22 figs

  12. Are internet prices sticky?

    OpenAIRE

    Lünnemann, Patrick; Wintr, Ladislav

    2006-01-01

    This paper studies the behaviour of Internet prices. It compares price rigidities on the Internet and in traditional brick-and-mortar stores and provides a cross-country perspective. The data set covers a broad range of items typically sold over the Internet. It includes more than 5 million daily price quotes downloaded from price comparison web sites in France, Germany, Italy, the UK and the US. The following results emerge from our analysis. First, and contrary to the recent findings for co...

  13. Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond

    DEFF Research Database (Denmark)

    Hong, Tao; Pinson, Pierre; Fan, Shu

    2016-01-01

    The energy industry has been going through a significant modernization process over the last decade. Its infrastructure is being upgraded rapidly. The supply, demand and prices are becoming more volatile and less predictable than ever before. Even its business model is being challenged fundamenta......The energy industry has been going through a significant modernization process over the last decade. Its infrastructure is being upgraded rapidly. The supply, demand and prices are becoming more volatile and less predictable than ever before. Even its business model is being challenged...... fundamentally. In this competitive and dynamic environment, many decision-making processes rely on probabilistic forecasts to quantify the uncertain future. Although most of the papers in the energy forecasting literature focus on point or singlevalued forecasts, the research interest in probabilistic energy...

  14. Load forecasting

    International Nuclear Information System (INIS)

    Mak, H.

    1995-01-01

    Slides used in a presentation at The Power of Change Conference in Vancouver, BC in April 1995 about the changing needs for load forecasting were presented. Technological innovations and population increase were said to be the prime driving forces behind the changing needs in load forecasting. Structural changes, market place changes, electricity supply planning changes, and changes in planning objectives were other factors discussed. It was concluded that load forecasting was a form of information gathering, that provided important market intelligence

  15. Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China.

    Directory of Open Access Journals (Sweden)

    Wei Wu

    Full Text Available Cases of hemorrhagic fever with renal syndrome (HFRS are widely distributed in eastern Asia, especially in China, Russia, and Korea. It is proved to be a difficult task to eliminate HFRS completely because of the diverse animal reservoirs and effects of global warming. Reliable forecasting is useful for the prevention and control of HFRS.Two hybrid models, one composed of nonlinear autoregressive neural network (NARNN and autoregressive integrated moving average (ARIMA the other composed of generalized regression neural network (GRNN and ARIMA were constructed to predict the incidence of HFRS in the future one year. Performances of the two hybrid models were compared with ARIMA model.The ARIMA, ARIMA-NARNN ARIMA-GRNN model fitted and predicted the seasonal fluctuation well. Among the three models, the mean square error (MSE, mean absolute error (MAE and mean absolute percentage error (MAPE of ARIMA-NARNN hybrid model was the lowest both in modeling stage and forecasting stage. As for the ARIMA-GRNN hybrid model, the MSE, MAE and MAPE of modeling performance and the MSE and MAE of forecasting performance were less than the ARIMA model, but the MAPE of forecasting performance did not improve.Developing and applying the ARIMA-NARNN hybrid model is an effective method to make us better understand the epidemic characteristics of HFRS and could be helpful to the prevention and control of HFRS.

  16. An Extrapolative Model of House Price Dynamics

    OpenAIRE

    Edward L. Glaeser; Charles G. Nathanson

    2015-01-01

    A modest approximation by homebuyers leads house prices to display three features that are present in the data but usually missing from perfectly rational models: momentum at one-year horizons, mean reversion at five-year horizons, and excess longer-term volatility relative to fundamentals. Valuing a house involves forecasting the current and future demand to live in the surrounding area. Buyers forecast using past transaction prices. Approximating buyers do not adjust for the expectations of...

  17. Arima model and exponential smoothing method: A comparison

    Science.gov (United States)

    Wan Ahmad, Wan Kamarul Ariffin; Ahmad, Sabri

    2013-04-01

    This study shows the comparison between Autoregressive Moving Average (ARIMA) model and Exponential Smoothing Method in making a prediction. The comparison is focused on the ability of both methods in making the forecasts with the different number of data sources and the different length of forecasting period. For this purpose, the data from The Price of Crude Palm Oil (RM/tonne), Exchange Rates of Ringgit Malaysia (RM) in comparison to Great Britain Pound (GBP) and also The Price of SMR 20 Rubber Type (cents/kg) with three different time series are used in the comparison process. Then, forecasting accuracy of each model is measured by examinethe prediction error that producedby using Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute deviation (MAD). The study shows that the ARIMA model can produce a better prediction for the long-term forecasting with limited data sources, butcannot produce a better prediction for time series with a narrow range of one point to another as in the time series for Exchange Rates. On the contrary, Exponential Smoothing Method can produce a better forecasting for Exchange Rates that has a narrow range of one point to another for its time series, while itcannot produce a better prediction for a longer forecasting period.

  18. Another look on the relationships between oil prices and energy prices

    International Nuclear Information System (INIS)

    Lahiani, Amine; Miloudi, Anthony; Benkraiem, Ramzi; Shahbaz, Muhammad

    2017-01-01

    This paper employs the Quantile Autoregressive Distributed Lags (QARDL) model developed recently by Cho et al. (2015) to investigate the pass-through of oil prices to a set of energy prices. This approach allows analyzing simultaneously short-term connections and long-run cointegrating relationships across a range of quantiles. It also provides insights on the short-run predictive power of oil prices in predicting energy prices while accounting for the cointegration between oil prices and each of the considered energy prices in low, medium and high quantiles. Two key findings emerge from this paper. First, all considered energy prices are shown to be cointegrated with oil price across quantiles meaning that a stationaryequilibriumrelationship exists between single energy price and oil price. Second, we find evidence that oil price is a significant predictor of individual petroleum products prices and natural gas in the short run. This paper has important policy implications for forecasters, energy policy-makers and portfolio managers. - Highlights: • The pass-through of oil prices to a set of energy prices is investigated for US economy. • All considered energy prices are shown to be cointegrated with oil price across quantiles. • Oil price is a significant predictor of individual petroleum products prices in the short run. • Oil price also predicts natural gas prices in the short run.

  19. Projections of the energy prices

    International Nuclear Information System (INIS)

    Jankauskas, V.

    1996-01-01

    This article deals with the trends of the main fuel prices development in the Western European markets. There are two possible price development scenarios presented in the article. Transportation costs of various internationally traded fuels from various sources (Russia, Western Europe) are estimated and their most feasible values are considered. Fuel prices for the final big consumers are calculated adding the domestic distribution costs. Trends of heat and electricity price development in Lithuania during the period of 1991-1995 are analyzed. Forecasts of the electricity generation and supply costs are calculated according to various scenarios. Electricity prices will be lowest in the case of the further operation of the Ignalina NPP and low fuel prices in international markets. (author). 8 refs., 14 figs., 4 tabs

  20. Availability, price and affordability of cardiovascular medicines: A comparison across 36 countries using WHO/HAI data

    Directory of Open Access Journals (Sweden)

    Cameron Alexandra

    2010-06-01

    Full Text Available Abstract Background The global burden of cardiovascular disease (CVD continues to rise. Successful treatment of CVD requires adequate pharmaceutical management. The aim was to examine the availability, pricing and affordability of cardiovascular medicines in developing countries using the standardized data collected according to the World Health Organization/Health Action International methodology. Methods The following medicines were included: atenolol, captopril, hydrochlorothiazide, losartan and nifedipine. Data from 36 countries were analyzed. Outcome measures were percentage availability, price ratios to international reference prices and number of day's wages needed by the lowest-paid unskilled government worker to purchase one month of chronic treatment. Patient prices were adjusted for inflation and purchasing power, procurement prices only for inflation. Data were analyzed for both generic and originator brand products and the public and private sector and summarized by World Bank Income Groups. Results For all measures, there was great variability across surveys. The overall availability of cardiovascular medicines was poor (mean 26.3% in public sector, 57.3% private sector. Procurement prices were very competitive in some countries, whereas others consistently paid high prices. Patient prices were generally substantially higher than international references prices; some countries, however, performed well. Chronic treatment with anti-hypertensive medication cost more than one day's wages in many cases. In particular when monotherapy is insufficient, treatment became unaffordable. Conclusions The results of this study emphasize the need of focusing attention and financing on making chronic disease medicines accessible, in particular in the public sector. Several policy options are suggested to reach this goal.

  1. Comparison of Monetary Policy Actions and Central Bank Communication on Tackling Asset Price Bubbles-Evidence from China's Stock Market.

    Science.gov (United States)

    Sun, Ou; Liu, Zhixin

    2016-01-01

    We examine the different effects of monetary policy actions and central bank communication on China's stock market bubbles with a Time-varying Parameter SVAR model. We find that with negative responses of fundamental component and positive responses of bubble component of asset prices, contractionary monetary policy induces the observed stock prices to rise during periods of large bubbles. By contrast, central bank communication acts on the market through expectation guidance and has more significant effects on stock prices in the long run, which implies that central bank communication be used as an effective long-term instrument for the central bank's policymaking.

  2. Price fairness

    OpenAIRE

    Diller, Hermann

    2013-01-01

    Purpose – The purpose of this article is to integrate the various strands of fair price research into a concise conceptual model. Design/methodology/approach – The proposed price fairness model is based on a review of the fair pricing literature, incorporating research reported in not only English but also German. Findings – The proposed fair price model depicts seven components of a fair price: distributive fairness, consistent behaviour, personal respect and regard for the partner, fair dea...

  3. Exploring Online Shopping Behaviour within the Context of Online Advertisement, Customer Service Experience Consciousness and Price Comparison Websites: Perspectives from Young Female Shoppers in the Zlínský Region

    OpenAIRE

    Christian Nedu Osakwe; Miloslava Chovancová

    2015-01-01

    Online shopping behaviour within the context of price comparison websites presents an interesting area of consumer behaviour, yet very little research has been done in this area. Hence, the focus of this paper was to critically explore the impact of online advertisement and customer service experience consciousness on young female shoppers' intention to use price comparison websites as a purchase decision-making tool within the online retail environment. Our sample consisted of 123 female res...

  4. A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia.

    Science.gov (United States)

    Aboagye-Sarfo, Patrick; Mai, Qun; Sanfilippo, Frank M; Preen, David B; Stewart, Louise M; Fatovich, Daniel M

    2015-10-01

    To develop multivariate vector-ARMA (VARMA) forecast models for predicting emergency department (ED) demand in Western Australia (WA) and compare them to the benchmark univariate autoregressive moving average (ARMA) and Winters' models. Seven-year monthly WA state-wide public hospital ED presentation data from 2006/07 to 2012/13 were modelled. Graphical and VARMA modelling methods were used for descriptive analysis and model fitting. The VARMA models were compared to the benchmark univariate ARMA and Winters' models to determine their accuracy to predict ED demand. The best models were evaluated by using error correction methods for accuracy. Descriptive analysis of all the dependent variables showed an increasing pattern of ED use with seasonal trends over time. The VARMA models provided a more precise and accurate forecast with smaller confidence intervals and better measures of accuracy in predicting ED demand in WA than the ARMA and Winters' method. VARMA models are a reliable forecasting method to predict ED demand for strategic planning and resource allocation. While the ARMA models are a closely competing alternative, they under-estimated future ED demand. Copyright © 2015 Elsevier Inc. All rights reserved.

  5. High-order fuzzy time-series based on multi-period adaptation model for forecasting stock markets

    Science.gov (United States)

    Chen, Tai-Liang; Cheng, Ching-Hsue; Teoh, Hia-Jong

    2008-02-01

    Stock investors usually make their short-term investment decisions according to recent stock information such as the late market news, technical analysis reports, and price fluctuations. To reflect these short-term factors which impact stock price, this paper proposes a comprehensive fuzzy time-series, which factors linear relationships between recent periods of stock prices and fuzzy logical relationships (nonlinear relationships) mined from time-series into forecasting processes. In empirical analysis, the TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) and HSI (Heng Seng Index) are employed as experimental datasets, and four recent fuzzy time-series models, Chen’s (1996), Yu’s (2005), Cheng’s (2006) and Chen’s (2007), are used as comparison models. Besides, to compare with conventional statistic method, the method of least squares is utilized to estimate the auto-regressive models of the testing periods within the databases. From analysis results, the performance comparisons indicate that the multi-period adaptation model, proposed in this paper, can effectively improve the forecasting performance of conventional fuzzy time-series models which only factor fuzzy logical relationships in forecasting processes. From the empirical study, the traditional statistic method and the proposed model both reveal that stock price patterns in the Taiwan stock and Hong Kong stock markets are short-term.

  6. Forecasting with Option-Implied Information

    DEFF Research Database (Denmark)

    Christoffersen, Peter; Jacobs, Kris; Chang, Bo Young

    2013-01-01

    This chapter surveys the methods available for extracting information from option prices that can be used in forecasting. We consider option-implied volatilities, skewness, kurtosis, and densities. More generally, we discuss how any forecasting object that is a twice differentiable function...... of the future realization of the underlying risky asset price can utilize option-implied information in a well-defined manner. Going beyond the univariate option-implied density, we also consider results on option-implied covariance, correlation and beta forecasting, as well as the use of option......-implied information in cross-sectional forecasting of equity returns. We discuss how option-implied information can be adjusted for risk premia to remove biases in forecasting regressions....

  7. Dynamic Pricing

    DEFF Research Database (Denmark)

    Sharifi, Reza; Anvari-Moghaddam, Amjad; Fathi, S. Hamid

    2017-01-01

    Dynamic pricing scheme, also known as real-time pricing (RTP), can be more efficient and technically beneficial than the other price-based schemes (such as flat-rate or time-of-use (TOU) pricing) for enabling demand response (DR) actions. Over the past few years, advantages of RTP-based schemes h...... of dynamic pricing can lead to increased willingness of consumers to participate in DR programs which in turn improve the operation of liberalized electricity markets.......Dynamic pricing scheme, also known as real-time pricing (RTP), can be more efficient and technically beneficial than the other price-based schemes (such as flat-rate or time-of-use (TOU) pricing) for enabling demand response (DR) actions. Over the past few years, advantages of RTP-based schemes...

  8. Determination and forecast of agricultural land prices

    Directory of Open Access Journals (Sweden)

    Bastiaan Philip Reydon

    2014-09-01

    Full Text Available O objetivo do presente trabalho foi discutir e aplicar a metodologia de preços hedônicos para determinação e previsão do preço da terra rural em mercados específicos.A sua importância decorre do fato de que não existem no Brasil informações oficiais ou fidedignas sobre preços praticados nos negócios com imóveis rurais. Essa metodologia de preços hedônicos utiliza um modelo econométrico de regressão múltipla, tendo como variável dependente o preço por hectare, e como variáveis explicativas as relacionadas ao meio físico (solo, clima e relevo, à produção (sistemas de produção, localização, acesso, à infraestrutura do imóvel e às expectativas (situação regional, investimentos locais. A aplicação de tal metodologia a uma zona homogênea do Estado do Maranhão implicou um modelo parcimonioso, em que cinco variáveis independentes permitiram a explicação de 70% da variância do preço por hectare da terra rural.

  9. Housing price forecastability: A factor analysis

    DEFF Research Database (Denmark)

    Bork, Lasse; Møller, Stig Vinther

    of the model stays high at longer horizons. The estimated factors are strongly statistically signi…cant according to a bootstrap resampling method which takes into account that the factors are estimated regressors. The simple three-factor model also contains substantial out-of-sample predictive power...

  10. Expectations and bubbles in asset pricing experiments

    NARCIS (Netherlands)

    Hommes, C.; Sonnemans, J.; Tuinstra, J.; van de Velden, H.

    2008-01-01

    We present results on expectation formation in a controlled experimental environment. In each period subjects are asked to predict the next price of a risky asset. The realized market price is derived from an unknown market equilibrium equation with feedback from individual forecasts. In most

  11. Comparison of landslide forecasting services in Piedmont (Italy) and Norway, illustrated by events in late spring 2013

    Science.gov (United States)

    Devoli, Graziella; Tiranti, Davide; Cremonini, Roberto; Sund, Monica; Boje, Søren

    2018-05-01

    Only few countries operate systematically national and regional forecasting services for rainfall-induced landslides (i.e., debris flows, debris avalanches and shallow slides), among them Norway and Italy. In Norway, the Norwegian Water Resources and Energy Directorate (NVE) operates a landslide forecasting service at national level. In Italy, the Regional Agency for Environmental Protection, ARPA Piemonte, is responsible for issuing landslide warnings for the Piedmont region, located in northwestern Italy. A daily hazard assessment is performed, describing both expected awareness level and type of landslide hazard for a selected warning region. Both services provide regular landslide hazard assessments based on a combination of quantitative thresholds and daily rainfall forecasts together with qualitative expert analysis. Daily warning reports are published at http://www.arpa.piemonte.gov.it/rischinaturali and http://www.varsom.no, last access: 7 May 2018. In spring 2013, ARPA Piemonte and the NVE issued warnings for hydro-meteorological hazards due to the arrival of a deep and large low-pressure system, called herein Vb cyclone. This kind of weather system is known to produce the largest floods in Europe. Less known is that this weather pattern can trigger landslides as well. In this study, we present the experiences of NVE and ARPA Piemonte in the late spring of 2013. The Vb cyclone influenced weather throughout Europe over a long period, from the end of April until the beginning of June 2013. However, major affects were observed in the first half part of this period in Piedmont, while in Norway, major damage was reported from 15 May to 2 June 2013. Floods and landslides significantly damaged roads, railways, buildings and other infrastructure in both countries. This case study shows that large synoptic pattern can produce different natural hazards in different parts of Europe, from sandstorms at low latitudes, to flood and landslides when the system moves

  12. Comparison of landslide forecasting services in Piedmont (Italy and Norway, illustrated by events in late spring 2013

    Directory of Open Access Journals (Sweden)

    G. Devoli

    2018-05-01

    Full Text Available Only few countries operate systematically national and regional forecasting services for rainfall-induced landslides (i.e., debris flows, debris avalanches and shallow slides, among them Norway and Italy. In Norway, the Norwegian Water Resources and Energy Directorate (NVE operates a landslide forecasting service at national level. In Italy, the Regional Agency for Environmental Protection, ARPA Piemonte, is responsible for issuing landslide warnings for the Piedmont region, located in northwestern Italy. A daily hazard assessment is performed, describing both expected awareness level and type of landslide hazard for a selected warning region. Both services provide regular landslide hazard assessments based on a combination of quantitative thresholds and daily rainfall forecasts together with qualitative expert analysis. Daily warning reports are published at http://www.arpa.piemonte.gov.it/rischinaturali and http://www.varsom.no, last access: 7 May 2018. In spring 2013, ARPA Piemonte and the NVE issued warnings for hydro-meteorological hazards due to the arrival of a deep and large low-pressure system, called herein Vb cyclone. This kind of weather system is known to produce the largest floods in Europe. Less known is that this weather pattern can trigger landslides as well. In this study, we present the experiences of NVE and ARPA Piemonte in the late spring of 2013. The Vb cyclone influenced weather throughout Europe over a long period, from the end of April until the beginning of June 2013. However, major affects were observed in the first half part of this period in Piedmont, while in Norway, major damage was reported from 15 May to 2 June 2013. Floods and landslides significantly damaged roads, railways, buildings and other infrastructure in both countries. This case study shows that large synoptic pattern can produce different natural hazards in different parts of Europe, from sandstorms at low latitudes, to flood and landslides when

  13. Forecasting stock return volatility: A comparison between the roles of short-term and long-term leverage effects

    Science.gov (United States)

    Pan, Zhiyuan; Liu, Li

    2018-02-01

    In this paper, we extend the GARCH-MIDAS model proposed by Engle et al. (2013) to account for the leverage effect in short-term and long-term volatility components. Our in-sample evidence suggests that both short-term and long-term negative returns can cause higher future volatility than positive returns. Out-of-sample results show that the predictive ability of GARCH-MIDAS is significantly improved after taking the leverage effect into account. The leverage effect for short-term volatility component plays more important role than the leverage effect for long-term volatility component in affecting out-of-sample forecasting performance.

  14. Exposure Forecaster

    Data.gov (United States)

    U.S. Environmental Protection Agency — The Exposure Forecaster Database (ExpoCastDB) is EPA's database for aggregating chemical exposure information and can be used to help with chemical exposure...

  15. Strategic Forecasting

    DEFF Research Database (Denmark)

    Duus, Henrik Johannsen

    2016-01-01

    Purpose: The purpose of this article is to present an overview of the area of strategic forecasting and its research directions and to put forward some ideas for improving management decisions. Design/methodology/approach: This article is conceptual but also informed by the author’s long contact...... and collaboration with various business firms. It starts by presenting an overview of the area and argues that the area is as much a way of thinking as a toolbox of theories and methodologies. It then spells out a number of research directions and ideas for management. Findings: Strategic forecasting is seen...... as a rebirth of long range planning, albeit with new methods and theories. Firms should make the building of strategic forecasting capability a priority. Research limitations/implications: The article subdivides strategic forecasting into three research avenues and suggests avenues for further research efforts...

  16. End user prices in liberalised energy markets

    Energy Technology Data Exchange (ETDEWEB)

    Lijesen, M.G. [Afdeling Energie en Grondstoffen, Centraal Planbureau CPB, Den Haag (Netherlands)

    2002-12-01

    As European energy markets move towards deregulation, energy prices shift from classic 'cost plus' prices towards market prices. We develop a model for the retail and wholesale energy markets in Europe, based on Bertrand competition in a two part pricing structure with switching costs. We use the model to forecast end user electricity and natural gas prices and find that the introduction of competition in energy retail and wholesale markets will decrease standing charges, lowering total costs for energy users. A larger number of entrants, a cost advantage for one of the suppliers, or lower switching costs reduces standing charges further.

  17. The oil price

    International Nuclear Information System (INIS)

    Alba, P.

    2000-01-01

    Statistical analysis cannot, alone, provide an oil price forecast. So, one needs to understand the fundamental phenomena which control the past trends since the end of world war II After a first period during which oil, thanks to its abundance, was able to increase its market share at the expense of other energies, the first oil shock reflects the rarefaction of oil resource with the tilting of the US production curve from growth to decline. Since then, the new situation is that of a ''cohabitation'' between oil and the other energies with the oil price, extremely volatile, reflecting the trial and error adjustment of the market share left to the other energies. Such a context may explain the recent oil price surge but the analogy between the US oil situation at the time of the first shock and that existing today for the world outside Middle East suggest another possibility, that of a structural change with higher future oil prices. The authors examine these two possibilities, think that the oil price will reflect both as long as one or the other will not become proven, and conclude with a series of political recommendations. (authors)

  18. The macroeconomic effects of oil price fluctuations on a small open oil-producing country. The case of Trinidad and Tobago

    International Nuclear Information System (INIS)

    Lorde, Troy; Thomas, Chrystol; Jackman, Mahalia

    2009-01-01

    Using vector autoregressive (VAR) methodology, this paper empirically investigates the macroeconomic effects of oil price fluctuations on Trinidad and Tobago. Overall, we find that the price of oil is a major determinant of economic activity of the country. Our impulse response functions suggest that following a positive oil price shock, output falls within the first two years followed by positive and growing response. We also investigate the macroeconomic impact of oil price volatility. Results suggest that an unanticipated shock to oil price volatility brings about random swings in the macroeconomy; however, only government revenue and the price level exhibit significant responses. With regard to the magnitude of the responses, shocks to oil price volatility tend to yield smaller macroeconomic impacts in comparison to shocks to oil prices. Variance decompositions suggest that the price of oil is a major component of forecast variation for most macroeconomic variables. Finally, Granger-causality tests indicate causality from oil prices to output and oil prices to government revenue. (author)

  19. Transfer Pricing

    DEFF Research Database (Denmark)

    Nielsen, Søren Bo

    2014-01-01

    Against a background of rather mixed evidence about transfer pricing practices in multinational enterprises (MNEs) and varying attitudes on the part of tax authorities, this paper explores how multiple aims in transfer pricing can be pursued across four different transfer pricing regimes. A MNE h...

  20. Gold prices

    OpenAIRE

    Joseph G. Haubrich

    1998-01-01

    The price of gold commands attention because it serves as an indicator of general price stability or inflation. But gold is also a commodity, used in jewelry and by industry, so demand and supply affect its pricing and need to be considered when gold is a factor in monetary policy decisions.

  1. Comparison of the Performance of SOEs Banks and Private Banks and its Influece to the Stock Prices

    Directory of Open Access Journals (Sweden)

    Husnul Insan

    2017-09-01

    Full Text Available Banking industry is one of the industries which show the presence of tight competition. The competition itself can be seen from the number of banks which operates in Indonesia in 2014, that is 119 banks. The aim of doing this research is to analyze the different financial performances between Significant effect on the level of 5% and Private banks, and also to analyze the variable influence of bank finance performances to the stock price. The analyzing method which will be used on this research is radar and regression analyzing graphic illustration techniques. The result of this research shows that financial performances of SEOs Banks is better than Private Banks according to the profitability, productivity, and growth potential aspect, while on the aspect of liquidity, private banks has a better performance than SOEs banks. In terms of solvability, both banks have the same financial performances. According to the regression analysis, it is known that the bank financial performance on CAS and NIM variable has a positive and significant influence to the stock prices, while on LDR, CRR, CAR, DRR, and ROA variables they have a negative and significant influence to the stock prices. This shows that the investors have made those variables as a benchmark in their investing policies and they tend to pursue the stocks from SOEs banks rather than private banks, because generally SOEs banks have a better financial performance than private banks.Keywords: regression analysis, bank, stock prices, financial performances, radar method

  2. Comparison of Nutrient Content and Cost of Home-Packed Lunches to Reimbursable School Lunch Nutrient Standards and Prices

    Science.gov (United States)

    Johnson, Cara M.; Bednar, Carolyn; Kwon, Junehee; Gustof, Alissa

    2009-01-01

    Purpose: The purpose of this study was to compare nutrient content and cost of home-packed lunches to nutrient standards and prices for reimbursable school lunches. Methods: Researchers observed food and beverage contents of 333 home packed lunches at four north Texas elementary schools. Nutritionist Pro was used to analyze lunches for calories,…

  3. Comparison of physicochemical characteristics and consumer perception of rice samples with different countries of origin and prices.

    Science.gov (United States)

    Jang, Eun-Hee; Lim, Seung-Taik; Kim, Sang Sook

    2012-06-01

    Rice is the only food item which is not liberalized in Korea. To prepare for future liberalization of the rice market in Korea, the physicochemical characteristics and consumer perception of four domestic rice samples were compared with those of 11 foreign rice samples. Additionally, the influences of country of origin (CO) and price on purchase intent (PI) and willingness to pay (WTP) were investigated. Consumer acceptance of rice samples from Japan was generally higher than that of rice samples from other countries. In an informed test, PI was affected by price and CO, while WTP was affected mostly by price. The PI decreased with the price provided while the WTP increased. In a blind test, consumers evaluated PI and WTP according to sensory liking. The PI was higher in the informed test than in the blind test in all domestic samples. Generally, consumer acceptance was highly correlated with fat acidity (r = - 0.76), protein content (r = - 0.72), and b value (r = - 0.64) of the rice samples. Further efforts to increase the quality of rice are recommended for survival in the global market after liberalization of rice, even though consumers showed loyalty for PI of domestic samples. Copyright © 2011 Society of Chemical Industry.

  4. Estimating Price Elasticity of Demand for Motor Fuel in the Transport Sectors

    Directory of Open Access Journals (Sweden)

    Olga Vasilyevna Mazurova

    2015-03-01

    Full Text Available Modeling of long-term forecasts of prices and demand on regional energy markets requires accounting for the future changes in the interactions between the greater economy and its energy sector, along with the possible emergence of new factors and specific regional features determining those interactions. The proposed approach allows the study of a correlation between demand and prices for motor fuel, taking into account the competition of energy carriers, the dynamics of energy prices, resource constraints, the use of new technologies and the uncertainty of input data. The main feature of the proposed approach is the combined estimation of the price elasticity of demand for motor fuel with optimization of fuel supply in the region. Thus the author determined elasticity of demand based on the comparison of economic efficiency of the use of different fuels. The study includes results of experimental calculations and forecasted price according to demand for motor fuel in freight transportation for the expected development conditions of the Far Eastern federal district

  5. Forecasting Analysis of Shanghai Stock Index Based on ARIMA Model

    Directory of Open Access Journals (Sweden)

    Li Chenggang

    2017-01-01

    Full Text Available Prediction and analysis of the Shanghai Composite Index is conducive for investors to investing in the stock market, and providing investors with reference. This paper selects Shanghai Composite Index monthly closing price from Jan, 2005 to Oct, 2016 to construct ARIMA model. This paper carries on the forecast of the last three monthly closing price of Shanghai Stock Index that have occurred, and compared it with the actual value, which tests the accuracy and feasibility of the model in the short term Shanghai Stock Index forecast. At last, this paper uses the ARIMA model to forecast the Shanghai Composite Index closing price of the last two months in 2016.

  6. Energy forecast. Final report; Energiudsigten. Slutrapport

    Energy Technology Data Exchange (ETDEWEB)

    2010-04-15

    A number of instruments, i.e. Internet, media campaigns, boxes displaying electricity prices (SEE1) and spot contract has been tested for households to shift their electricity consumption to times when prices are low. Of the implemented media campaigns, only the daily viewing of Energy forecast on TV had an impact. Consumers gained greater knowledge of electricity prices and electricity consumption loads, but only showed little interest in shifting electricity consumption. However, a measurable effect appeared at night with the group that had both concluded a spot contract and received an SEE1. These factors increase the awareness of the price of electricity and the possibility of shifting electricity consumption. (Energy 10)

  7. EU pharmaceutical expenditure forecast.

    Science.gov (United States)

    Urbinati, Duccio; Rémuzat, Cécile; Kornfeld, Åsa; Vataire, Anne-Lise; Cetinsoy, Laurent; Aballéa, Samuel; Mzoughi, Olfa; Toumi, Mondher

    2014-01-01

    With constant incentives for healthcare payers to contain their pharmaceutical budgets, forecasting has become critically important. Some countries have, for instance, developed pharmaceutical horizon scanning units. The objective of this project was to build a model to assess the net effect of the entrance of new patented medicinal products versus medicinal products going off-patent, with a defined forecast horizon, on selected European Union (EU) Member States' pharmaceutical budgets. This model took into account population ageing, as well as current and future country-specific pricing, reimbursement, and market access policies (the project was performed for the European Commission; see http://ec.europa.eu/health/healthcare/key_documents/index_en.htm). In order to have a representative heterogeneity of EU Member States, the following countries were selected for the analysis: France, Germany, Greece, Hungary, Poland, Portugal, and the United Kingdom. A forecasting period of 5 years (2012-2016) was chosen to assess the net pharmaceutical budget impact. A model for generics and biosimilars was developed for each country. The model estimated a separate and combined effect of the direct and indirect impacts of the patent cliff. A second model, estimating the sales development and the risk of development failure, was developed for new drugs. New drugs were reviewed individually to assess their clinical potential and translate it into commercial potential. The forecast was carried out according to three perspectives (healthcare public payer, society, and manufacturer), and several types of distribution chains (retail, hospital, and combined retail and hospital). Probabilistic and deterministic sensitivity analyses were carried out. According to the model, all countries experienced drug budget reductions except Poland (+€41 million). Savings were expected to be the highest in the United Kingdom (-€9,367 million), France (-€5,589 million), and, far behind them

  8. Forecasting Interest Rates Using Geostatistical Techniques

    Directory of Open Access Journals (Sweden)

    Giuseppe Arbia

    2015-11-01

    Full Text Available Geostatistical spatial models are widely used in many applied fields to forecast data observed on continuous three-dimensional surfaces. We propose to extend their use to finance and, in particular, to forecasting yield curves. We present the results of an empirical application where we apply the proposed method to forecast Euro Zero Rates (2003–2014 using the Ordinary Kriging method based on the anisotropic variogram. Furthermore, a comparison with other recent methods for forecasting yield curves is proposed. The results show that the model is characterized by good levels of predictions’ accuracy and it is competitive with the other forecasting models considered.

  9. International drug price comparisons: quality assessment Comparación internacional de precios de medicamentos: evaluación de la calidad

    Directory of Open Access Journals (Sweden)

    Márcio Machado

    2011-01-01

    Full Text Available OBJECTIVE: To quantitatively summarize results (i.e., prices and affordability reported from international drug price comparison studies and assess their methodological quality. METHODS: A systematic search of the most relevant databases-Medline, Embase, International Pharmaceutical Abstracts (IPA, and Scopus, from their inception to May 2009-was conducted to identify original research comparing international drug prices. International drug price information was extracted and recorded from accepted papers. Affordability was reported as drug prices adjusted for income. Study quality was assessed using six criteria: use of similar countries, use of a representative sample of drugs, selection of specific types of prices, identification of drug packaging, different weights on price indices, and the type of currency conversion used. RESULTS: Of the 1 828 studies identified, 21 were included. Only one study adequately addressed all quality issues. A large variation in study quality was observed due to the many methods used to conduct the drug price comparisons, such as different indices, economic parameters, price types, basket of drugs, and more. Thus, the quality of published studies was considered poor. Results varied across studies, but generally, higher income countries had higher drug prices. However, after adjusting drug prices for affordability, higher income countries had more affordable prices than lower income countries. CONCLUSIONS: Differences between drug prices and affordability in different countries were found. Low income countries reported less affordability of drugs, leaving room for potential problems with drug access, and consequently, a negative impact on health. The quality of the literature on this topic needs improvement.OBJETIVO: Resumir cuantitativamente los resultados (p. ej., precios y asequibilidad presentados en estudios de comparación internacional de precios de medicamentos y evaluar su calidad metodológica. M

  10. Volatility in energy prices

    International Nuclear Information System (INIS)

    Duffie, D.

    1999-01-01

    This chapter with 58 references reviews the modelling and empirical behaviour of volatility in energy prices. Constant volatility and stochastic volatility are discussed. Markovian models of stochastic volatility are described and the different classes of Markovian stochastic volatility model are examined including auto-regressive volatility, option implied and forecasted volatility, Garch volatility, Egarch volatility, multivariate Garch volatility, and stochastic volatility and dynamic hedging policies. Other volatility models and option hedging are considered. The performance of several stochastic volatility models as applied to heating oil, light oil, natural gas, electricity and light crude oil are compared

  11. Operational hydrological forecasting in Bavaria. Part I: Forecast uncertainty

    Science.gov (United States)

    Ehret, U.; Vogelbacher, A.; Moritz, K.; Laurent, S.; Meyer, I.; Haag, I.

    2009-04-01

    In Bavaria, operational flood forecasting has been established since the disastrous flood of 1999. Nowadays, forecasts based on rainfall information from about 700 raingauges and 600 rivergauges are calculated and issued for nearly 100 rivergauges. With the added experience of the 2002 and 2005 floods, awareness grew that the standard deterministic forecast, neglecting the uncertainty associated with each forecast is misleading, creating a false feeling of unambiguousness. As a consequence, a system to identify, quantify and communicate the sources and magnitude of forecast uncertainty has been developed, which will be presented in part I of this study. In this system, the use of ensemble meteorological forecasts plays a key role which will be presented in part II. Developing the system, several constraints stemming from the range of hydrological regimes and operational requirements had to be met: Firstly, operational time constraints obviate the variation of all components of the modeling chain as would be done in a full Monte Carlo simulation. Therefore, an approach was chosen where only the most relevant sources of uncertainty were dynamically considered while the others were jointly accounted for by static error distributions from offline analysis. Secondly, the dominant sources of uncertainty vary over the wide range of forecasted catchments: In alpine headwater catchments, typically of a few hundred square kilometers in size, rainfall forecast uncertainty is the key factor for forecast uncertainty, with a magnitude dynamically changing with the prevailing predictability of the atmosphere. In lowland catchments encompassing several thousands of square kilometers, forecast uncertainty in the desired range (usually up to two days) is mainly dependent on upstream gauge observation quality, routing and unpredictable human impact such as reservoir operation. The determination of forecast uncertainty comprised the following steps: a) From comparison of gauge

  12. Ensuring capacity adequacy during energy transition in mature power markets: a social efficiency comparison of scarcity pricing and capacity mechanism

    International Nuclear Information System (INIS)

    Petitet, Marie; Finon, Dominique; Janssen, Tanguy

    2016-01-01

    This paper analyses how a capacity market mechanism can address security of supply objectives in the case of an energy transition scenario which combines both high energy efficiency efforts which stabilise demand in a context of mature markets and rapid increase of renewables share. The exogenous entry of variable renewables introduces a new challenge in matter of security of supply during peak hours. To analyse this situation, power markets are simulated on the long term with a model based on System Dynamics modelling which integrates both new investment and closure decisions. This last trait is an originality of the model which is very relevant to study market maturity. The addition of a capacity mechanism in a market architecture with price cap is compared to scarcity pricing in different situations. Simulations are performed for two different cases: a case without any exogenous closure of existing power plants and a case with exogenous retirements which create a need of new investments. Under the assumption of a risk-neutral investor, the results indicate that compared to an energy-only market with price cap set at euro 3,000/MWh, energy-only with scarcity pricing and capacity mechanism are two efficient market designs to reach an acceptable level of loss of load. Besides, the results highlight that the advantage of one design on the other in terms of social efficiency depends on the future scenarios which are simulated. Moreover, the results illustrates that the three market designs lead to different level of risk for peaking units, suggesting that including risk aversion is a relevant further step in the modelling. (authors)

  13. Addressing forecast uncertainty impact on CSP annual performance

    Science.gov (United States)

    Ferretti, Fabio; Hogendijk, Christopher; Aga, Vipluv; Ehrsam, Andreas

    2017-06-01

    This work analyzes the impact of weather forecast uncertainty on the annual performance of a Concentrated Solar Power (CSP) plant. Forecast time series has been produced by a commercial forecast provider using the technique of hindcasting for the full year 2011 in hourly resolution for Ouarzazate, Morocco. Impact of forecast uncertainty has been measured on three case studies, representing typical tariff schemes observed in recent CSP projects plus a spot market price scenario. The analysis has been carried out using an annual performance model and a standard dispatch optimization algorithm based on dynamic programming. The dispatch optimizer has been demonstrated to be a key requisite to maximize the annual revenues depending on the price scenario, harvesting the maximum potential out of the CSP plant. Forecasting uncertainty affects the revenue enhancement outcome of a dispatch optimizer depending on the error level and the price function. Results show that forecasting accuracy of direct solar irradiance (DNI) is important to make best use of an optimized dispatch but also that a higher number of calculation updates can partially compensate this uncertainty. Improvement in revenues can be significant depending on the price profile and the optimal operation strategy. Pathways to achieve better performance are presented by having more updates both by repeatedly generating new optimized trajectories but also more often updating weather forecasts. This study shows the importance of working on DNI weather forecasting for revenue enhancement as well as selecting weather services that can provide multiple updates a day and probabilistic forecast information.

  14. Modeling spot markets for electricity and pricing electricity derivatives

    Science.gov (United States)

    Ning, Yumei

    Spot prices for electricity have been very volatile with dramatic price spikes occurring in restructured market. The task of forecasting electricity prices and managing price risk presents a new challenge for market players. The objectives of this dissertation are: (1) to develop a stochastic model of price behavior and predict price spikes; (2) to examine the effect of weather forecasts on forecasted prices; (3) to price electricity options and value generation capacity. The volatile behavior of prices can be represented by a stochastic regime-switching model. In the model, the means of the high-price and low-price regimes and the probabilities of switching from one regime to the other are specified as functions of daily peak load. The probability of switching to the high-price regime is positively related to load, but is still not high enough at the highest loads to predict price spikes accurately. An application of this model shows how the structure of the Pennsylvania-New Jersey-Maryland market changed when market-based offers were allowed, resulting in higher price spikes. An ARIMA model including temperature, seasonal, and weekly effects is estimated to forecast daily peak load. Forecasts of load under different assumptions about weather patterns are used to predict changes of price behavior given the regime-switching model of prices. Results show that the range of temperature forecasts from a normal summer to an extremely warm summer cause relatively small increases in temperature (+1.5%) and load (+3.0%). In contrast, the increases in prices are large (+20%). The conclusion is that the seasonal outlook forecasts provided by NOAA are potentially valuable for predicting prices in electricity markets. The traditional option models, based on Geometric Brownian Motion are not appropriate for electricity prices. An option model using the regime-switching framework is developed to value a European call option. The model includes volatility risk and allows changes

  15. Long-range forecast of all India summer monsoon rainfall using adaptive neuro-fuzzy inference system: skill comparison with CFSv2 model simulation and real-time forecast for the year 2015

    Science.gov (United States)

    Chaudhuri, S.; Das, D.; Goswami, S.; Das, S. K.

    2016-11-01

    All India summer monsoon rainfall (AISMR) characteristics play a vital role for the policy planning and national economy of the country. In view of the significant impact of monsoon system on regional as well as global climate systems, accurate prediction of summer monsoon rainfall has become a challenge. The objective of this study is to develop an adaptive neuro-fuzzy inference system (ANFIS) for long range forecast of AISMR. The NCEP/NCAR reanalysis data of temperature, zonal and meridional wind at different pressure levels have been taken to construct the input matrix of ANFIS. The membership of the input parameters for AISMR as high, medium or low is estimated with trapezoidal membership function. The fuzzified standardized input parameters and the de-fuzzified target output are trained with artificial neural network models. The forecast of AISMR with ANFIS is compared with non-hybrid multi-layer perceptron model (MLP), radial basis functions network (RBFN) and multiple linear regression (MLR) models. The forecast error analyses of the models reveal that ANFIS provides the best forecast of AISMR with minimum prediction error of 0.076, whereas the errors with MLP, RBFN and MLR models are 0.22, 0.18 and 0.73 respectively. During validation with observations, ANFIS shows its potency over the said comparative models. Performance of the ANFIS model is verified through different statistical skill scores, which also confirms the aptitude of ANFIS in forecasting AISMR. The forecast skill of ANFIS is also observed to be better than Climate Forecast System version 2. The real-time forecast with ANFIS shows possibility of deficit (65-75 cm) AISMR in the year 2015.

  16. Generation Mix Study Focusing on Nuclear Power by Practical Peak Forecast

    International Nuclear Information System (INIS)

    Shin, Jung Ho; Roh, Myung Sub

    2013-01-01

    The excessive underestimation can lead to a range of problem; expansion of LNG plant requiring short construction period, the following increase of electricity price, low reserve margin and inefficient configuration of power source. With regard to nuclear power, the share of the stable and economic base load plant, nuclear power, can reduce under the optimum level. Amongst varied factors which contribute to the underestimate, immoderate target for demand side management (DSM) including double deduction of the constraint amount by DSM from peak demand forecast is one of the causes. The hypothesis in this study is that the better optimum generation mix including the adequate share of nuclear power can be obtained under the condition of the peak demand forecast without deduction of DSM target because this forecast is closer to the actual peak demand. In this study, the hypothesis is verified with comparison between peak demand forecast before (or after) DSM target application and the actual peak demand in the 3 rd through 5 th BPE from 2006 to 2010. Furthermore, this research compares and analyzes several generation mix in 2027 focusing on the nuclear power by a few conditions using the WASP-IV program on the basis of the 6 th BPE in 2013. According to the comparative analysis on the peak demand forecast and actual peak demand from 2006 to 2010, the peak demand forecasts without the deduction of the DSM target is closer to the actual peak demand than the peak demand forecasts considering the DSM target in the 3 th , 4 th , 5 th entirely. In addition, the generation mix until 2027 is examined by the WASP-IV. As a result of the program run, when considering the peak demand forecast without DSM reflection, since the base load plants including nuclear power take up adequate proportion, stable and economic supply of electricity can be achieved. On the contrary, in case of planning based on the peak demand forecast with DSM reflected and then compensating the shortage by

  17. Generation Mix Study Focusing on Nuclear Power by Practical Peak Forecast

    Energy Technology Data Exchange (ETDEWEB)

    Shin, Jung Ho; Roh, Myung Sub [KEPCO International Nuclear Graduate School, Ulsan (Korea, Republic of)

    2013-10-15

    The excessive underestimation can lead to a range of problem; expansion of LNG plant requiring short construction period, the following increase of electricity price, low reserve margin and inefficient configuration of power source. With regard to nuclear power, the share of the stable and economic base load plant, nuclear power, can reduce under the optimum level. Amongst varied factors which contribute to the underestimate, immoderate target for demand side management (DSM) including double deduction of the constraint amount by DSM from peak demand forecast is one of the causes. The hypothesis in this study is that the better optimum generation mix including the adequate share of nuclear power can be obtained under the condition of the peak demand forecast without deduction of DSM target because this forecast is closer to the actual peak demand. In this study, the hypothesis is verified with comparison between peak demand forecast before (or after) DSM target application and the actual peak demand in the 3{sup rd} through 5{sup th} BPE from 2006 to 2010. Furthermore, this research compares and analyzes several generation mix in 2027 focusing on the nuclear power by a few conditions using the WASP-IV program on the basis of the 6{sup th} BPE in 2013. According to the comparative analysis on the peak demand forecast and actual peak demand from 2006 to 2010, the peak demand forecasts without the deduction of the DSM target is closer to the actual peak demand than the peak demand forecasts considering the DSM target in the 3{sup th}, 4{sup th}, 5{sup th} entirely. In addition, the generation mix until 2027 is examined by the WASP-IV. As a result of the program run, when considering the peak demand forecast without DSM reflection, since the base load plants including nuclear power take up adequate proportion, stable and economic supply of electricity can be achieved. On the contrary, in case of planning based on the peak demand forecast with DSM reflected and then

  18. IFIS Model-Plus: A Web-Based GUI for Visualization, Comparison and Evaluation of Distributed Flood Forecasts and Hindcasts

    Science.gov (United States)

    Krajewski, W. F.; Della Libera Zanchetta, A.; Mantilla, R.; Demir, I.

    2017-12-01

    This work explores the use of hydroinformatics tools to provide an user friendly and accessible interface for executing and assessing the output of realtime flood forecasts using distributed hydrological models. The main result is the implementation of a web system that uses an Iowa Flood Information System (IFIS)-based environment for graphical displays of rainfall-runoff simulation results for both real-time and past storm events. It communicates with ASYNCH ODE solver to perform large-scale distributed hydrological modeling based on segmentation of the terrain into hillslope-link hydrologic units. The cyber-platform also allows hindcast of model performance by testing multiple model configurations and assumptions of vertical flows in the soils. The scope of the currently implemented system is the entire set of contributing watersheds for the territory of the state of Iowa. The interface provides resources for visualization of animated maps for different water-related modeled states of the environment, including flood-waves propagation with classification of flood magnitude, runoff generation, surface soil moisture and total water column in the soil. Additional tools for comparing different model configurations and performing model evaluation by comparing to observed variables at monitored sites are also available. The user friendly interface has been published to the web under the URL http://ifis.iowafloodcenter.org/ifis/sc/modelplus/.

  19. Transfer Pricing

    DEFF Research Database (Denmark)

    Rohde, Carsten; Rossing, Christian Plesner

    trade internally as the units have to decide what prices should be paid for such inter-unit transfers. One important challenge is to uncover the consequences that different transfer prices have on the willingness in the organizational units to coordinate activities and trade internally. At the same time...... the determination of transfer price will affect the size of the profit or loss in the organizational units and thus have an impact on the evaluation of managers‟ performance. In some instances the determination of transfer prices may lead to a disagreement between coordination of the organizational units...

  20. Reply to "Comment on 'Nonparametric forecasting of low-dimensional dynamical systems' ".

    Science.gov (United States)

    Berry, Tyrus; Giannakis, Dimitrios; Harlim, John

    2016-03-01

    In this Reply we provide additional results which allow a better comparison of the diffusion forecast and the "past-noise" forecasting (PNF) approach for the El Niño index. We remark on some qualitative differences between the diffusion forecast and PNF, and we suggest an alternative use of the diffusion forecast for the purposes of forecasting the probabilities of extreme events.

  1. Price Intransparency, Consumer Decision Making and European Consumer Law

    OpenAIRE

    Boom, Willem

    2011-01-01

    textabstractPrice comparison is a basic element of competition. For comparison to work, at least prices need to be transparent. Moreover, price is usually a focal point in consumer thinking and deciding on transactions. Hence, obfuscating prices can be detrimental to consumers. Therefore, it is vital for policymakers to know how transparent pricing is in reality. Commercial practices involving price intransparency can be detrimental to consumer decision making and may be associated with marke...

  2. Exploration of approaches to adjusting brand-name drug prices in Mainland of China: based on comparison and analysis of some brand-name drug prices of Mainland and Taiwan, China.

    Science.gov (United States)

    Weng, Geng; Han, Sheng; Pu, Run; Pan, Wynn H T; Shi, Luwen

    2014-01-01

    Under the circumstance of the New Medical Reform in Mainland of China, lowering drug prices has become an approach to relieving increase of medical expenses, and lowering brand-name medication price is a key strategy. This study, by comparing and analyzing brand-name medication prices between Mainland of China and Taiwan, explores how to adjust brand-name medication prices in Mainland of China in the consideration of the drug administrative strategies in Taiwan. By selecting brand-name drug with generic name and dose types matched in Mainland and Taiwan, calculate the average unit price and standard deviation and test it with the paired t-test. In the mean time, drug administrative strategies between Mainland and Taiwan are also compared systematically. Among the 70 brand-name medications with generic names and matched dose types, 54 are at higher prices in Mainland of China than Taiwan, which is statistically significant in t-test. Also, among the 47 medications with all of matched generic names, dose types, and manufacturing enterprises, 38 are at higher prices in Mainland than Taiwan, and the gap is also statistically significant in t-test. In Mainland of China, brand-name medication took cost-plus pricing and price-based price adjustment, while in Taiwan, brand-name medication took internal and external reference pricing and market-based price adjustment. Brand-name drug prices were higher in Mainland of China than in Taiwan. The adjustment strategies of drug prices are scientific in Taiwan and are worth reference by Mainland of China.

  3. Forecast errors in IEA-countries' energy consumption

    DEFF Research Database (Denmark)

    Linderoth, Hans

    2002-01-01

    Every year Policy of IEA Countries includes a forecast of the energy consumption in the member countries. Forecasts concerning the years 1985,1990 and 1995 can now be compared to actual values. The second oil crisis resulted in big positive forecast errors. The oil price drop in 1986 did not have...... the small value is often the sum of large positive and negative errors. Almost no significant correlation is found between forecast errors in the 3 years. Correspondingly, no significant correlation coefficient is found between forecasts errors in the 3 main energy sectors. Therefore, a relatively small...

  4. Electricity consumption forecasting in Italy using linear regression models

    Energy Technology Data Exchange (ETDEWEB)

    Bianco, Vincenzo; Manca, Oronzio; Nardini, Sergio [DIAM, Seconda Universita degli Studi di Napoli, Via Roma 29, 81031 Aversa (CE) (Italy)

    2009-09-15

    The influence of economic and demographic variables on the annual electricity consumption in Italy has been investigated with the intention to develop a long-term consumption forecasting model. The time period considered for the historical data is from 1970 to 2007. Different regression models were developed, using historical electricity consumption, gross domestic product (GDP), gross domestic product per capita (GDP per capita) and population. A first part of the paper considers the estimation of GDP, price and GDP per capita elasticities of domestic and non-domestic electricity consumption. The domestic and non-domestic short run price elasticities are found to be both approximately equal to -0.06, while long run elasticities are equal to -0.24 and -0.09, respectively. On the contrary, the elasticities of GDP and GDP per capita present higher values. In the second part of the paper, different regression models, based on co-integrated or stationary data, are presented. Different statistical tests are employed to check the validity of the proposed models. A comparison with national forecasts, based on complex econometric models, such as Markal-Time, was performed, showing that the developed regressions are congruent with the official projections, with deviations of {+-}1% for the best case and {+-}11% for the worst. These deviations are to be considered acceptable in relation to the time span taken into account. (author)

  5. Electricity consumption forecasting in Italy using linear regression models

    International Nuclear Information System (INIS)

    Bianco, Vincenzo; Manca, Oronzio; Nardini, Sergio

    2009-01-01

    The influence of economic and demographic variables on the annual electricity consumption in Italy has been investigated with the intention to develop a long-term consumption forecasting model. The time period considered for the historical data is from 1970 to 2007. Different regression models were developed, using historical electricity consumption, gross domestic product (GDP), gross domestic product per capita (GDP per capita) and population. A first part of the paper considers the estimation of GDP, price and GDP per capita elasticities of domestic and non-domestic electricity consumption. The domestic and non-domestic short run price elasticities are found to be both approximately equal to -0.06, while long run elasticities are equal to -0.24 and -0.09, respectively. On the contrary, the elasticities of GDP and GDP per capita present higher values. In the second part of the paper, different regression models, based on co-integrated or stationary data, are presented. Different statistical tests are employed to check the validity of the proposed models. A comparison with national forecasts, based on complex econometric models, such as Markal-Time, was performed, showing that the developed regressions are congruent with the official projections, with deviations of ±1% for the best case and ±11% for the worst. These deviations are to be considered acceptable in relation to the time span taken into account. (author)

  6. FORECASTING THE PERFORMANCE OF AN OIL FIELD, COMPARISON OF VARIOUS USED METHODS: THE CASE OF SHUANGHE OILFIELD, CHINA

    Directory of Open Access Journals (Sweden)

    Theoneste Nzayisenga

    2014-01-01

    Full Text Available While being the dominant source of energy, oil has also brought affluence and power to different societies. Energy produced from oil is fundamental to all parts of society. In the foreseeable future, the majority of energy will still come from oil production. Consequently, reliable methods for forecasting that production are crucial. Petroleum engineers have searched for simple but reliable way to predict oil production for a long time. Many methods have been developed in the latest decades and one common practice is decline curve analysis. Prediction of future production of petroleum wells is important for cost-effective operations of the petroleum industry. This work presents a comparative analysis of methods used to predict the performance of Shuanghe oilfield, China. Using decline curve analysis including three different methods: Arps empirical methods, LL-model and simplified model and the new simplified model, LL-Model, to crosscheck Arps exponential decline model prediction results. The results showed by the comparative analysis of predictions calculated proved LL-model to be the best predictor for Shuanghe oilfield since it takes into account more parameters than the old models used in this work. However, the subsurface information or parameters of the reservoir used in LL-model may not be available every time, therefore Arps models may apply as defined. In Shuanghe oilfield calculated average geological reserves N was estimated at 9449.41 ×104 tons, the average recoverable reserves NR were estimated to 4274.61×104 tons while the water cut was 97% and the water cut predicted by LL-model was 96.7%; not far from water flooding curves value. The exponential decline model showed recoverable reserves NR estimated around 4685.88×104 tons of oil while the decline phase of total development was estimated around 34 years which means that if the actual production conditions remain unchanged, Shuanghe oilfield would continue producing for

  7. Forecasting winds over nuclear power plants statistics

    International Nuclear Information System (INIS)

    Marais, Ch.

    1997-01-01

    In the event of an accident at nuclear power plant, it is essential to forecast the wind velocity at the level where the efflux occurs (about 100 m). At present meteorologists refine the wind forecast from the coarse grid of numerical weather prediction (NWP) models. The purpose of this study is to improve the forecasts by developing a statistical adaptation method which corrects the NWP forecasts by using statistical comparisons between wind forecasts and observations. The Multiple Linear Regression method is used here to forecast the 100 m wind at 12 and 24 hours range for three Electricite de France (EDF) sites. It turns out that this approach gives better forecasts than the NWP model alone and is worthy of operational use. (author)

  8. Electricity market price spike analysis by a hybrid data model and feature selection technique

    International Nuclear Information System (INIS)

    Amjady, Nima; Keynia, Farshid

    2010-01-01

    In a competitive electricity market, energy price forecasting is an important activity for both suppliers and consumers. For this reason, many techniques have been proposed to predict electricity market prices in the recent years. However, electricity price is a complex volatile signal owning many spikes. Most of electricity price forecast techniques focus on the normal price prediction, while price spike forecast is a different and more complex prediction process. Price spike forecasting has two main aspects: prediction of price spike occurrence and value. In this paper, a novel technique for price spike occurrence prediction is presented composed of a new hybrid data model, a novel feature selection technique and an efficient forecast engine. The hybrid data model includes both wavelet and time domain variables as well as calendar indicators, comprising a large candidate input set. The set is refined by the proposed feature selection technique evaluating both relevancy and redundancy of the candidate inputs. The forecast engine is a probabilistic neural network, which are fed by the selected candidate inputs of the feature selection technique and predict price spike occurrence. The efficiency of the whole proposed method for price spike occurrence forecasting is evaluated by means of real data from the Queensland and PJM electricity markets. (author)

  9. Constructing forward price curves in electricity markets

    DEFF Research Database (Denmark)

    Fleten, S.-E.; Lemming, Jørgen Kjærgaard

    2003-01-01

    We present and analyze a method for constructing approximated high-resolution forward price curves in electricity markets. Because a limited number of forward or futures contracts are traded in the market, only a limited picture of the theoretical continuous forward price curve is available...... to the analyst. Our method combines the information contained in observed bid and ask prices with information from the forecasts generated by bottom-up models. As an example, we use information concerning the shape of the seasonal variation from a bottom-up model to improve the forward price curve quoted...

  10. Constructing forward price curves in electricity markets

    International Nuclear Information System (INIS)

    Fleten, Stein-Erik; Lemming, Jacob

    2003-01-01

    We present and analyze a method for constructing approximated high-resolution forward price curves in electricity markets. Because a limited number of forward or futures contracts are traded in the market, only a limited picture of the theoretical continuous forward price curve is available to the analyst. Our method combines the information contained in observed bid and ask prices with information from the forecasts generated by bottom-up models. As an example, we use information concerning the shape of the seasonal variation from a bottom-up model to improve the forward price curve quoted on the Nordic power exchange

  11. Petroleum price

    International Nuclear Information System (INIS)

    Chevallier, B.

    2009-01-01

    The 'AFTP' conference on 'petroleum prices' organized by Total last March, tries to explain the different aspects of the crisis we undergo for July 2007 and its consequential effects on the petroleum markets (supply, demand evolvements, impacts on reserves, prices, refining...). (O.M.)

  12. Combining forecast weights: Why and how?

    Science.gov (United States)

    Yin, Yip Chee; Kok-Haur, Ng; Hock-Eam, Lim

    2012-09-01

    This paper proposes a procedure called forecast weight averaging which is a specific combination of forecast weights obtained from different methods of constructing forecast weights for the purpose of improving the accuracy of pseudo out of sample forecasting. It is found that under certain specified conditions, forecast weight averaging can lower the mean squared forecast error obtained from model averaging. In addition, we show that in a linear and homoskedastic environment, this superior predictive ability of forecast weight averaging holds true irrespective whether the coefficients are tested by t statistic or z statistic provided the significant level is within the 10% range. By theoretical proofs and simulation study, we have shown that model averaging like, variance model averaging, simple model averaging and standard error model averaging, each produces mean squared forecast error larger than that of forecast weight averaging. Finally, this result also holds true marginally when applied to business and economic empirical data sets, Gross Domestic Product (GDP growth rate), Consumer Price Index (CPI) and Average Lending Rate (ALR) of Malaysia.

  13. Price Intransparency, Consumer Decision Making and European Consumer Law

    NARCIS (Netherlands)

    W.H. van Boom (Willem)

    2011-01-01

    textabstractPrice comparison is a basic element of competition. For comparison to work, at least prices need to be transparent. Moreover, price is usually a focal point in consumer thinking and deciding on transactions. Hence, obfuscating prices can be detrimental to consumers. Therefore, it is

  14. World oil prices, precious metal prices and macroeconomy in Turkey

    International Nuclear Information System (INIS)

    Soytas, Ugur; Sari, Ramazan; Hammoudeh, Shawkat; Hacihasanoglu, Erk

    2009-01-01

    We examine the long- and short-run transmissions of information between the world oil price, Turkish interest rate, Turkish lira-US dollar exchange rate, and domestic spot gold and silver price. We find that the world oil price has no predictive power of the precious metal prices, the interest rate or the exchange rate market in Turkey. The results also show that the Turkish spot precious metals, exchange rate and bond markets do not also provide information that would help improve the forecasts of world oil prices in the long run. The findings suggest that domestic gold is also considered a safe haven in Turkey during devaluation of the Turkish lira, as it is globally. It is interesting to note that there does not seem to be any significant influence of developments in the world oil markets on Turkish markets in the short run either. However, transitory positive initial impacts of innovations in oil prices on gold and silver markets are observed. The short-run price transmissions between the world oil market and the Turkish precious metal markets have implications for policy makers in emerging markets and both local and global investors in the precious metals market and the oil market.

  15. An efficient and simplified model for forecasting using SRM

    International Nuclear Information System (INIS)

    Asif, H.M.; Hyat, M.F.; Ahmad, T.

    2014-01-01

    Learning form continuous financial systems play a vital role in enterprise operations. One of the most sophisticated non-parametric supervised learning classifiers, SVM (Support Vector Machines), provides robust and accurate results, however it may require intense computation and other resources. The heart of SLT (Statistical Learning Theory), SRM (Structural Risk Minimization )Principle can also be used for model selection. In this paper, we focus on comparing the performance of model estimation using SRM with SVR (Support Vector Regression) for forecasting the retail sales of consumer products. The potential benefits of an accurate sales forecasting technique in businesses are immense. Retail sales forecasting is an integral part of strategic business planning in areas such as sales planning, marketing research, pricing, production planning and scheduling. Performance comparison of support vector regression with model selection using SRM shows comparable results to SVR but in a computationally efficient manner. This research targeted the real life data to conclude the results after investigating the computer generated datasets for different types of model building. (author)

  16. An Efficient and Simplified Model for Forecasting using SRM

    Directory of Open Access Journals (Sweden)

    Hafiz Muhammad Shahzad Asif

    2014-01-01

    Full Text Available Learning form continuous financial systems play a vital role in enterprise operations. One of the most sophisticated non-parametric supervised learning classifiers, SVM (Support Vector Machines, provides robust and accurate results, however it may require intense computation and other resources. The heart of SLT (Statistical Learning Theory, SRM (Structural Risk Minimization Principle can also be used for model selection. In this paper, we focus on comparing the performance of model estimation using SRM with SVR (Support Vector Regression for forecasting the retail sales of consumer products. The potential benefits of an accurate sales forecasting technique in businesses are immense. Retail sales forecasting is an integral part of strategic business planning in areas such as sales planning, marketing research, pricing, production planning and scheduling. Performance comparison of support vector regression with model selection using SRM shows comparable results to SVR but in a computationally efficient manner. This research targeted the real life data to conclude the results after investigating the computer generated datasets for different types of model building

  17. Forecasting volatility for options valuation

    International Nuclear Information System (INIS)

    Belaifa, M.; Morimune, K.

    2006-01-01

    The petroleum sector plays a neuralgic role in the basement of world economies, and market actors (producers, intermediates, as well as consumers) are continuously subjected to the dynamics of unstable oil market. Huge amounts are being invested along the production chain to make one barrel of crude oil available to the end user. Adding to that are the effect of geopolitical dynamics as well as geological risks as expressed in terms of low chances of successful discoveries. In addition, fiscal regimes and regulations, technology and environmental concerns are also among some of the major factors that contribute to the substantial risk in the oil industry and render the market structure vulnerable to crises. The management of these vulnerabilities require modern tools to reduce risk to a certain level, which unfortunately is a non-zero value. The aim of this paper is, therefore, to provide a modern technique to capture the oil price stochastic volatility that can be implemented to value the exposure of an investor, a company, a corporate or a Government. The paper first analyses the regional dependence on oil prices, through a historical perspective and then looks at the evolution of pricing environment since the large price jumps of the 1970s. The main causes of oil prices volatility are treated in the third part of the paper. The rest of the article deals with volatility models and forecasts used in risk management, with an implication for pricing derivatives. (author)

  18. On the market impact of wind energy forecasts

    International Nuclear Information System (INIS)

    Jonsson, Tryggvi; Pinson, Pierre; Madsen, Henrik

    2010-01-01

    This paper presents an analysis of how day-ahead electricity spot prices are affected by day-ahead wind power forecasts. Demonstration of this relationship is given as a test case for the Western Danish price area of the Nord Pool's Elspot market. Impact on the average price behaviour is investigated as well as that on the distributional properties of the price. By using a non-parametric regression model to assess the effects of wind power forecasts on the average behaviour, the non-linearities and time variations in the relationship are captured well and the effects are shown to be quite substantial. Furthermore, by evaluating the distributional properties of the spot prices under different scenarios, the impact of the wind power forecasts on the price distribution is proved to be considerable. The conditional price distribution is moreover shown to be non-Gaussian. This implies that forecasting models for electricity spot prices for which parameters are estimated by a least squares techniques will not have Gaussian residuals. Hence the widespread assumption of Gaussian residuals from electricity spot price models is shown to be inadequate for these model types. The revealed effects are likely to be observable and qualitatively similar in other day-ahead electricity markets significantly penetrated by wind power. (author)

  19. A long-term view of worldwide fossil fuel prices

    International Nuclear Information System (INIS)

    Shafiee, Shahriar; Topal, Erkan

    2010-01-01

    This paper reviews a long-term trend of worldwide fossil fuel prices in the future by introducing a new method to forecast oil, natural gas and coal prices. The first section of this study analyses the global fossil fuel market and the historical trend of real and nominal fossil fuel prices from 1950 to 2008. Historical fossil fuel price analysis shows that coal prices are decreasing, while natural gas prices are increasing. The second section reviews previously available price modelling techniques and proposes a new comprehensive version of the long-term trend reverting jump and dip diffusion model. The third section uses the new model to forecast fossil fuel prices in nominal and real terms from 2009 to 2018. The new model follows the extrapolation of the historical sinusoidal trend of nominal and real fossil fuel prices. The historical trends show an increase in nominal/real oil and natural gas prices plus nominal coal prices, as well as a decrease in real coal prices. Furthermore, the new model forecasts that oil, natural gas and coal will stay in jump for the next couple of years and after that they will revert back to the long-term trend until 2018. (author)

  20. Gas prices and price process

    International Nuclear Information System (INIS)

    Groenewegen, G.G.

    1992-01-01

    On a conference (Gas for Europe in the 1990's) during the Gasexpo '91 the author held a speech of which the Dutch text is presented here. Attention is paid to the current European pricing methods (prices based on the costs of buying, transporting and distributing the natural gas and prices based on the market value, which is deducted from the prices of alternative fuels), and the transparency of the prices (lack of information on the way the prices are determined). Also attention is paid to the market signal transparency and gas-gas competition, which means a more or less free market of gas distribution. The risks of gas-to-gas competition for a long term price stability, investment policies and security of supply are discussed. Opposition against the Third Party Access (TPA), which is the program to implement gas-to-gas competition, is caused by the fear of natural gas companies for lower gas prices and lower profits. Finally attention is paid to government regulation and the activities of the European Commission (EC) in this matter. 1 fig., 6 ills., 1 tab

  1. Oil price prospects

    International Nuclear Information System (INIS)

    Toalster, J.

    1992-01-01

    In this paper, four different, popular approaches to the analysis of oil price movements will be considered and an alternative method will be proposed. Whilst we await the development of a rigorous theoretical framework within which to evaluate the phenomenon of oil price movements some progress may be effected by an amalgam of approaches, with the traditional supply and demand model being supplemented by observations regarding political and social developments in particular countries or regions, together with an assessment of emerging and prospective technological achievements. In this way it should be possible to identify the critical influences at work, from which it should also be possible to select either the single most important variable or combination of variables, affecting the oil price. Moreover, it is my belief that the crucial variables influencing the oil price almost certainly, are more likely to be political and social, rather than economic. In this context and notwithstanding the fact that there is only a minimal level of surplus productive capacity in the world oil industry at present (perhaps 1-2 million b/d albeit rising rapidly), it is reasonable to conclude that oil prices will average around $18-19 a barrel for North Sea Brent in 1992 and 1993, with oscillations of $2-4 a barrel either side, rising slightly in 1994 to $19-20 a barrel and to $20-21 a barrel in 1995. Thereafter, the most likely outcome is for a rise in line with inflation (say $ a barrel/annum) with no prospect of an upward spike, because demand will be weaker than most commentators expect up to the year 2000, whilst OPEC oil supplies will be substantially higher than the consensus forecast. (author)

  2. Portability, Salary and Asset Price Risk: A Continuous-Time Expected Utility Comparison of DB and DC Pension Plans

    Directory of Open Access Journals (Sweden)

    An Chen

    2015-03-01

    Full Text Available This paper compares two different types of private retirement plans from the perspective of a representative beneficiary: a defined benefit (DB and a defined contribution (DC plan. While salary risk is the main common risk factor in DB and DC pension plans, one of the key differences is that DB plans carry portability risks, whereas DC plans bear asset price risk. We model these tradeoffs explicitly in this paper and compare these two plans in a utility-based framework. Our numerical analysis focuses on answering the question of when the beneficiary is indifferent between the DB and DC plan. Most of our results confirm the findings in the existing literature, among which, e.g., portability losses considerably reduce the relative attractiveness of the DB plan. However, we also find that the attractiveness of the DB plan can decrease in the level of risk aversion, which is inconsistent with the existing literature.

  3. Comparison of power exchanges. Liquidity and prices in spot transactions; Stromboersen im Vergleich. Liquiditaet und Preise im Spothandel

    Energy Technology Data Exchange (ETDEWEB)

    Simon, Manuel

    2012-07-01

    The liberalization of the world's electricity markets in the past 20 years provided new challenges for operators of power distribution systems as well as producers: Especially in Europe, the integration of renewable energy sources is another challenge. The increasing importance of wind energy, solar energy and hydro power as a CO{sub 2} free energy source raises the question which market design is the best for the description of the constant fluctuations of these energy sources. As a result of liberalization, two market models are distinguished: the stock market model and the pool model. The author of the contribution under consideration analyzes empirically the power exchanges EEX (European energy Exchange, Leipzig, Federal Republic of Germany), Nordpool (Lysaker, Norway) and PJM (Norristown, Pennsylvania, USA). The parameters are determined which significantly affect the prices and the liquidity in the spot market.

  4. A Comparison of Algorithms for Controlling DSRs in a Control by Price Context Using Hardware-in-the-loop Simulation

    DEFF Research Database (Denmark)

    Sossan, Fabrizio; Bindner, Henrik W.

    2012-01-01

    , DSRs, are electric loads whose power consumption can be shifted without having a big impact on the primary services they are supplying and they are suitable for being controlled according the needs of regulating power in the electric power system. In this paper the performances and the aggregate...... response of a real office building; the experimental results of using a model predictive controller for heating a real building in a variable price context are also presented. This study is part of the Flexpower project whose aim is investigating the possibility of creating an electric market......With future increasing of electric energy production from fluctuating sources, the need for regulating power will rise and conventional power plants - that today provide all power system ancillary services - could not have the capability and the flexibility of providing it. Demand Side Resource...

  5. Natural gas pricing policies in Southeast Asia

    International Nuclear Information System (INIS)

    Pacudan, R.B.

    1998-01-01

    The very dynamic economies of Southeast Asia have recently been experiencing a rapid increase in energy demand. Parallel to this development, there has been an increase in the utilization of indigenous natural gas resources. This article reviews gas-pricing policies in the region, which partly explain the rise in gas utilization. Although diverse, energy pricing policies in Southeast Asia address the common objective of enhancing domestic gas production and utilization. The article concludes that a more rational gas-pricing policy framework is emerging in the region. In global terms, gas pricing in the region tends to converge in a market-related framework, despite the many different pricing objectives of individual countries, and the predominance of non-economic pricing objectives in certain countries (especially gas-rich nations). Specifically, governments have been flexible enough to follow global trends and initiate changes in contractual agreements (pricing and profit-sharing), giving oil companies more favourable terms, and encouraging continued private investment in gas development. At the same time, promotional pricing has also been used to increase utilization of gas, through set prices and adjusted taxes achieving a lower price level compared to substitute fuels. For an efficient gas-pricing mechanism, refinements in the pricing framework should be undertaken, as demand for gas approaches existing and/or forecast production capacities. (author)

  6. Price increase

    CERN Multimedia

    2006-01-01

    Please take note that after five years of stable prices at Restaurant No 1 a price increase will come into force on 1st January 2006. This increase has been agreed after discussions between the CSR (Comité de Surveillance des Restaurants) and the catering company Novae and will reflect the inflation rate of the last few years. In addition, a new children's menu will be introduced, as well as 'Max Havelaar' fair-trade coffee at a price of 1.70 CHF.

  7. Price increase

    CERN Multimedia

    2005-01-01

    Please take note that after five years of stable prices at Restaurant No 1 a price increase will come into force on 1st January 2006. This increase has been agreed after discussions between the CSR (Comité de Surveillance des Restaurants) and the catering company Novae and will reflect the inflation rate of the last few years. In addition, a new children's menu will be introduced as well as 'Max Havelaar' fair-trade coffee at a price of 1.70 CHF.

  8. Of Needles and Haystacks: Novel Techniques for Data-Rich Economic Forecasting Data-Rich Economic Forecasting

    NARCIS (Netherlands)

    P. Exterkate (Peter)

    2011-01-01

    textabstractThis thesis discusses various novel techniques for economic forecasting. The focus is on methods that exploit the information in large data sets effectively. Each of these methods is compared to established techniques for forecasting yields on U.S. Treasury Bills, housing prices,

  9. A framework for the assessment of the oil price

    International Nuclear Information System (INIS)

    Oliveira, A. de; Guimaraes Lodi, C.F.

    1994-01-01

    There is a wide diversity of interpretations in the literature about the mechanisms that govern oil prices. None of them has been able to produce reasonable price forecasts. Estimates of future oil prices, or at least of a range of prices, are essential for policy-making. A better understanding of the interplay of the forces that affect oil prices is absolutely necessary. This paper aims to offer a contribution to the improvement of this understanding. It suggests a rational analytical framework to estimate the future price of oil, based on the objectives and policies of the major players in the oil market. (author)

  10. Energy Price Comparisons. Study on the quality of comparison sites for electricity and natural gas on the Internet; Energieprijsvergelijkers. Onderzoek naar de kwaliteit van vergelijkingssites voor elektriciteit en gas op het internet

    Energy Technology Data Exchange (ETDEWEB)

    Boezaard, P.M.; Van Liere, M.M.; Van Steenpaal, D.F.J.M.

    2007-06-15

    Internet sites with comparisons of prices for electricity and natural gas appear to be important tools for consumers. On a regular basis the Dutch Office of Energy Regulation (DTe) carries out a study on the quality of such price comparison sites. In this report the results of the June 2007 study are summarized. [Dutch] Voor een goedwerkende markt is een goed geinformeerde consument van belang. Uit de Consumentenmonitor, kwalitatief onderzoek dat de NMa in 2006 heeft laten uitvoeren, is gebleken dat de consument bij een overstap vaak gebruik maakt van prijsvergelijkingssites. Het belang van dergelijke sites neemt alleen maar toe. Door een grotere differentiatie aan producten en meer variatie in prijsstelling wordt een goede vergelijking van energieleveranciers voor de gemiddelde consument steeds lastiger. Ook voor de prijsvergelijkingssites neemt de complexiteit toe. De Directie Toezicht Energie (DTe) van de Nederlandse Mededingingsautoriteit (NMa) onderzoekt sinds 2004 jaarlijks de kwaliteit van (prijs)vergelijkingssites voor elektriciteit en gas. Gezien de groeiende complexiteit van de prijsvergelijkingen is het van belang dat de NMa dit onderzoek ook in 2007 uitvoert. In dit rapport worden de resultaten van dit jaar gepresenteerd. Daarnaast heeft de NMa voor het eerst een handleiding met tips voor de consument als bijlage bij dit rapport opgenomen.

  11. Freemium Pricing

    DEFF Research Database (Denmark)

    Runge, Julian; Wagner, Stefan; Claussen, Jörg

    Firms commonly run field experiments to improve their freemium pricing schemes. However, they often lack a framework for analysis that goes beyond directly measurable outcomes and focuses on longer term profit. We aim to fill this gap by structuring existing knowledge on freemium pricing...... into a stylized framework. We apply the proposed framework in the analysis of a field experiment that contrasts three variations of a freemium pricing scheme and comprises about 300,000 users of a software application. Our findings indicate that a reduction of free product features increases conversion as well...... as viral activity, but reduces usage – which is in line with the framework’s predictions. Additional back-of-the-envelope profit estimations suggest that managers were overly optimistic about positive externalities from usage and viral activity in their choice of pricing scheme, leading them to give too...

  12. Modeling and forecasting petroleum futures volatility

    International Nuclear Information System (INIS)

    Sadorsky, Perry

    2006-01-01

    Forecasts of oil price volatility are important inputs into macroeconometric models, financial market risk assessment calculations like value at risk, and option pricing formulas for futures contracts. This paper uses several different univariate and multivariate statistical models to estimate forecasts of daily volatility in petroleum futures price returns. The out-of-sample forecasts are evaluated using forecast accuracy tests and market timing tests. The TGARCH model fits well for heating oil and natural gas volatility and the GARCH model fits well for crude oil and unleaded gasoline volatility. Simple moving average models seem to fit well in some cases provided the correct order is chosen. Despite the increased complexity, models like state space, vector autoregression and bivariate GARCH do not perform as well as the single equation GARCH model. Most models out perform a random walk and there is evidence of market timing. Parametric and non-parametric value at risk measures are calculated and compared. Non-parametric models outperform the parametric models in terms of number of exceedences in backtests. These results are useful for anyone needing forecasts of petroleum futures volatility. (author)

  13. DIREM's prices. Prices and margins of petroleum products in France and in the European Union

    International Nuclear Information System (INIS)

    2002-08-01

    This report presents in a series of graphs and tables the prices and margins of petroleum products in France and in the European Union (EU) according to the data compiled by the Direction of energy and mineral resources (DIREM) of the French general direction of energy and raw materials (DGEMP, Ministry of economy, finances and industry): evolution of crude prices, evolution of Rotterdam's quotation of petroleum products, raw margin of brent refining, French fuel prices (automotive and domestic fuels, evolution, comparison with EU and Rotterdam's prices), comparison with prices in other European countries, evolution of average retail prices in France. (J.S.)

  14. Exploring Online Shopping Behaviour within the Context of Online Advertisement, Customer Service Experience Consciousness and Price Comparison Websites: Perspectives from Young Female Shoppers in the Zlínský Region

    Directory of Open Access Journals (Sweden)

    Christian Nedu Osakwe

    2015-01-01

    Full Text Available Online shopping behaviour within the context of price comparison websites presents an interesting area of consumer behaviour, yet very little research has been done in this area. Hence, the focus of this paper was to critically explore the impact of online advertisement and customer service experience consciousness on young female shoppers’ intention to use price comparison websites as a purchase decision-making tool within the online retail environment. Our sample consisted of 123 female respondents of the Tomas Bata University students in Zlín, Czech Republic. From the sample, we found out using the Partial Least Square approach to Structural Equation Modelling (PLS-SEM that both online advertisement (Ads and customer service experience consciousness are directly and positively related to online shoppers’ intention to use price comparison websites. Hence, our two stated hypotheses were supported based on the outcome of the structural model. Apparently, customer service experience consciousness is a better predictor of female shoppers’ intention to use price comparison websites compared to online advertising. Nonetheless, we have highlighted the need for a further research based on the study’s limitations.

  15. State-level electricity demand forecasting model. [For 1980, 1985, 1990

    Energy Technology Data Exchange (ETDEWEB)

    Nguyen, H. D.

    1978-01-01

    This note briefly describes the Oak Ridge National Laboratory (ORNL) state-level electricity demand (SLED) forecasting model developed for the Nuclear Regulatory Commission. Specifically, the note presents (1) the special features of the model, (2) the methodology used to forecast electricity demand, and (3) forecasts of electricity demand and average price by sector for 15 states for 1980, 1985, 1990.

  16. Commodity derivatives pricing with inventory effects

    DEFF Research Database (Denmark)

    Bach, Christian; Dziubinski, Matt P.

    We introduce tractable models for commodity derivatives pricing with inventory and volatility eects, and illustrate with applications to the oil market. We contribute to the existing literature in several respects. First, whereas the previous literature uses futures data for investigating...... the relationship between inventory and volatility, we use the information available in options traded on futures. Second, performance assessment in the previous literature has primarily evolved around explaining moments of data or forecasting prices of futures. Instead, we asses the performance of our model...... by considering both the ability of explaining prices in-sample and out-of-sample - assessing both the pricing-performance and the hedging-performance of the models. Third, we model the futures surface rather than the spot price process, and from the no-arbitrage relationship between spot and futures prices we...

  17. A PERFORMANCE COMPARISON BETWEEN ARTIFICIAL NEURAL NETWORKS AND MULTIVARIATE STATISTICAL METHODS IN FORECASTING FINANCIAL STRENGTH RATING IN TURKISH BANKING SECTOR

    Directory of Open Access Journals (Sweden)

    MELEK ACAR BOYACIOĞLU

    2013-06-01

    Full Text Available Financial strength rating indicates the fundamental financial strength of a bank. The aim of financial strength rating is to measure a bank’s fundamental financial strength excluding the external factors. External factors can stem from the working environment or can be linked with the outside protective support mechanisms. With the evaluation, the rating of a bank free from outside supportive factors is being sought. Also the financial fundamental, franchise value, the variety of assets and working environment of a bank are being evaluated in this context. In this study, a model has been developed in order to predict the financial strength rating of Turkish banks. The methodology of this study is as follows: Selecting variables to be used in the model, creating a data set, choosing the techniques to be used and the evaluation of classification success of the techniques. It is concluded that the artificial neural network system shows a better performance in terms of classification of financial strength rating in comparison to multivariate statistical methods in the raining set. On the other hand, there is no meaningful difference could be found in the validation set in which the prediction performances of the employed techniques are tested.

  18. Forecasting volatility of crude oil markets

    International Nuclear Information System (INIS)

    Kang, Sang Hoon; Kang, Sang-Mok; Yoon, Seong-Min

    2009-01-01

    This article investigates the efficacy of a volatility model for three crude oil markets - Brent, Dubai, and West Texas Intermediate (WTI) - with regard to its ability to forecast and identify volatility stylized facts, in particular volatility persistence or long memory. In this context, we assess persistence in the volatility of the three crude oil prices using conditional volatility models. The CGARCH and FIGARCH models are better equipped to capture persistence than are the GARCH and IGARCH models. The CGARCH and FIGARCH models also provide superior performance in out-of-sample volatility forecasts. We conclude that the CGARCH and FIGARCH models are useful for modeling and forecasting persistence in the volatility of crude oil prices. (author)

  19. Forecasting volatility of crude oil markets

    Energy Technology Data Exchange (ETDEWEB)

    Kang, Sang Hoon [Department of Business Administration, Gyeongsang National University, Jinju, 660-701 (Korea); Kang, Sang-Mok; Yoon, Seong-Min [Department of Economics, Pusan National University, Busan, 609-735 (Korea)

    2009-01-15

    This article investigates the efficacy of a volatility model for three crude oil markets - Brent, Dubai, and West Texas Intermediate (WTI) - with regard to its ability to forecast and identify volatility stylized facts, in particular volatility persistence or long memory. In this context, we assess persistence in the volatility of the three crude oil prices using conditional volatility models. The CGARCH and FIGARCH models are better equipped to capture persistence than are the GARCH and IGARCH models. The CGARCH and FIGARCH models also provide superior performance in out-of-sample volatility forecasts. We conclude that the CGARCH and FIGARCH models are useful for modeling and forecasting persistence in the volatility of crude oil prices. (author)

  20. Detecting Chaos from Agricultural Product Price Time Series

    Directory of Open Access Journals (Sweden)

    Xin Su

    2014-12-01

    Full Text Available Analysis of the characteristics of agricultural product price volatility and trend forecasting are necessary to formulate and implement agricultural price control policies. Taking wholesale cabbage prices as an example, a multiple test methodology has been adopted to identify the nonlinearity, fractality, and chaos of the data. The approaches used include the R/S analysis, the BDS test, the power spectra, the recurrence plot, the largest Lyapunov exponent, the Kolmogorov entropy, and the correlation dimension. The results show that there is chaos in agricultural wholesale price data, which provides a good theoretical basis for selecting reasonable forecasting models as prediction techniques based on chaos theory can be applied to forecasting agricultural prices.

  1. A trend discontinuity: The mystery of natural gas prices

    International Nuclear Information System (INIS)

    Steffes, D.W.

    1995-01-01

    For the last fifteen years, the natural gas price forecasting experts have had a terrible record of forecasting future natural gas prices. (In the early 80's, the gas price was forecasted to be over $10/MMBtu in the late 80's). To make matters even worse, they can't seem to understand why the price is what it is, even in hindsight. If these experts can't even get it right in hindsight, how can one ever expect to get it right in foresight? It is concluded that the traditional laws of supply and demand don't work very well in this new quasi-regulated natural gas industry. Evidently, Social Influences and Political Influences are more important than the Economic Influence on natural gas prices

  2. The rationality of EIA forecasts under symmetric and asymmetric loss

    International Nuclear Information System (INIS)

    Auffhammer, Maximilian

    2007-01-01

    The United States Energy Information Administration publishes annual forecasts of nationally aggregated energy consumption, production, prices, intensity and GDP. These government issued forecasts often serve as reference cases in the calibration of simulation and econometric models, which climate and energy policy are based on. This study tests for rationality of published EIA forecasts under symmetric and asymmetric loss. We find strong empirical evidence of asymmetric loss for oil, coal and electricity prices as well as natural gas consumption, electricity sales, GDP and energy intensity. (author)

  3. The rationality of EIA forecasts under symmetric and asymmetric loss

    Energy Technology Data Exchange (ETDEWEB)

    Auffhammer, Maximilian [Department of Agricultural and Resource Economics, University of California, 207 Giannini Hall 3310, Berkeley, CA 94720 (United States)

    2007-05-15

    The United States Energy Information Administration publishes annual forecasts of nationally aggregated energy consumption, production, prices, intensity and GDP. These government issued forecasts often serve as reference cases in the calibration of simulation and econometric models, which climate and energy policy are based on. This study tests for rationality of published EIA forecasts under symmetric and asymmetric loss. We find strong empirical evidence of asymmetric loss for oil, coal and electricity prices as well as natural gas consumption, electricity sales, GDP and energy intensity. (author)

  4. Electric power systems advanced forecasting techniques and optimal generation scheduling

    CERN Document Server

    Catalão, João P S

    2012-01-01

    Overview of Electric Power Generation SystemsCláudio MonteiroUncertainty and Risk in Generation SchedulingRabih A. JabrShort-Term Load ForecastingAlexandre P. Alves da Silva and Vitor H. FerreiraShort-Term Electricity Price ForecastingNima AmjadyShort-Term Wind Power ForecastingGregor Giebel and Michael DenhardPrice-Based Scheduling for GencosGovinda B. Shrestha and Songbo QiaoOptimal Self-Schedule of a Hydro Producer under UncertaintyF. Javier Díaz and Javie

  5. PyForecastTools

    Energy Technology Data Exchange (ETDEWEB)

    2017-09-22

    The PyForecastTools package provides Python routines for calculating metrics for model validation, forecast verification and model comparison. For continuous predictands the package provides functions for calculating bias (mean error, mean percentage error, median log accuracy, symmetric signed bias), and for calculating accuracy (mean squared error, mean absolute error, mean absolute scaled error, normalized RMSE, median symmetric accuracy). Convenience routines to calculate the component parts (e.g. forecast error, scaled error) of each metric are also provided. To compare models the package provides: generic skill score; percent better. Robust measures of scale including median absolute deviation, robust standard deviation, robust coefficient of variation and the Sn estimator are all provided by the package. Finally, the package implements Python classes for NxN contingency tables. In the case of a multi-class prediction, accuracy and skill metrics such as proportion correct and the Heidke and Peirce skill scores are provided as object methods. The special case of a 2x2 contingency table inherits from the NxN class and provides many additional metrics for binary classification: probability of detection, probability of false detection, false alarm ration, threat score, equitable threat score, bias. Confidence intervals for many of these quantities can be calculated using either the Wald method or Agresti-Coull intervals.

  6. Retrospective evaluation of appliance price trends

    International Nuclear Information System (INIS)

    Dale, Larry; Antinori, Camille; McNeil, Michael; McMahon, James E.; Sydny Fujita, K.

    2009-01-01

    Real prices of major appliances (refrigerators, dishwashers, heating and cooling equipment) have been falling since the late 1970s despite increases in appliance efficiency and other quality variables. This paper demonstrates that historic increases in efficiency over time, including those resulting from minimum efficiency standards, incur smaller price increases than were expected by the Department of Energy (DOE) forecasts made in conjunction with standards. This effect can be explained by technological innovation, which lowers the cost of efficiency, and by market changes contributing to lower markups and economies of scale in production of higher efficiency units. We reach four principal conclusions about appliance trends and retail price setting: 1.For the past several decades, the retail price of appliances has been steadily falling while efficiency has been increasing. 2.Past retail price predictions made by the DOE analyses of efficiency standards, assuming constant prices over time, have tended to overestimate retail prices. 3.The average incremental price to increase appliance efficiency has declined over time. DOE technical support documents have typically overestimated this incremental price and retail prices. 4.Changes in retail markups and economies of scale in production of more efficient appliances may have contributed to declines in prices of efficient appliances

  7. Uranium price trends for use in strategy analyses

    International Nuclear Information System (INIS)

    James, R.A.

    1979-09-01

    Long-term price forecasts for mined uranium are quoted. These will be used in Ontario Hydro's nuclear fuel cycle strategy analyses. They are, of necessity, speculative. The accuracy of the forecasts is considered adequate for long-term strategy analyses, but not for other purposes. (auth)

  8. The fractal feature and price trend in the gold future market at the Shanghai Futures Exchange (SFE)

    Science.gov (United States)

    Wu, Binghui; Duan, Tingting

    2017-05-01

    The price of gold future is affected by many factors, which include the fluctuation of gold price and the change of trading environment. Fractal analysis can help investors gain better understandings of the price fluctuation and make reasonable investment decisions in the gold future market. After analyzing gold future price from January 2th, 2014 to April 12th, 2016 at the Shanghai Futures Exchange (SFE) in China, the conclusion is drawn that the gold future market has sustainability in each trading day, with all Hurst indexes greater than 0.5. The changing features of Hurst index indicate the sustainability of gold future market is strengthened first and weakened then. As a complicatedly nonlinear system, the gold future market can be well reflected by Elman neural network, which is capable of memorizing previous prices and particularly suited for forecasting time series in comparison with other types of neural networks. After analyzing the price trend in the gold future market, the results show that the relative error between the actual value of gold future and the predictive value of Elman neural network is smaller. This model that has a better performance in data fitting and predication, can help investors analyze and foresee the price tendency in the gold future market.

  9. kosh Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  10. kpdt Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  11. kewr Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  12. kiso Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  13. kpga Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  14. kbkw Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  15. ktcl Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  16. pgwt Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  17. kpsp Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  18. kbih Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  19. kdnl Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  20. kart Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  1. kilm Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  2. kpne Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  3. kabi Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  4. ptpn Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  5. kblf Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  6. panc Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  7. kpbi Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  8. kgdv Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  9. kcmx Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  10. kdls Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  11. koaj Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  12. krhi Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  13. kbpk Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  14. khuf Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  15. kbpi Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  16. ktrk Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  17. kwmc Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  18. katy Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  19. tjmz Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  20. kdet Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...