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Sample records for previous forecasts compares

  1. A parimutuel gambling perspective to compare probabilistic seismicity forecasts

    Science.gov (United States)

    Zechar, J. Douglas; Zhuang, Jiancang

    2014-10-01

    Using analogies to gaming, we consider the problem of comparing multiple probabilistic seismicity forecasts. To measure relative model performance, we suggest a parimutuel gambling perspective which addresses shortcomings of other methods such as likelihood ratio, information gain and Molchan diagrams. We describe two variants of the parimutuel approach for a set of forecasts: head-to-head, in which forecasts are compared in pairs, and round table, in which all forecasts are compared simultaneously. For illustration, we compare the 5-yr forecasts of the Regional Earthquake Likelihood Models experiment for M4.95+ seismicity in California.

  2. How to compare what seems incomparable in seasonal hydrological forecasting?

    Science.gov (United States)

    Crochemore, Louise; Pechlivanidis, Ilias; Ramos, Maria-Helena

    2017-04-01

    A number of climate forecasting systems have been developed at the global scale allowing the production of seasonal hydrological services at the regional, national and continental scales. With these services becoming increasingly available to dissemination institutes and occasionally directly to the public, forecasters have been requesting information on the reliability of the forecasts, particularly when they need to select one system for a specific application. The quality of the forecasts depends on the hydrological model used and, consequently, on its setup, structure, objective, and performance. These characteristics can be very different among models, adding a degree of complexity to any model output inter-comparison analysis. Here, we propose a framework to compare outputs from different modelling systems. We compare the seasonal streamflow forecasts produced by a continentally-calibrated complex model (E-HYPE) and a regionally-calibrated parsimonious model (GR6J) to forecast streamflow in a set of French catchments. Streamflow forecasts are obtained by using bias adjusted ECMWF System 4 seasonal precipitation and temperature forecasts as input to the E-HYPE and GR6J hydrological models. We first identify within the forecasting chain the origin of the differences between the two hydrological systems. We use the evaluation of forecast skill to highlight and isolate the differences in meteorological forcing, initial hydrological conditions and historical model performance, respectively. Forecast skill is thus evaluated by considering different benchmarks based on: i) historical observed streamflow, ii) historical simulated streamflow, and iii) the Extended Streamflow Prediction (ESP) system which uses meteorological climatology as input to the hydrological models. We also present the dependence of forecast quality (i.e., sharpness and reliability) on the hydrological models used. Lastly, we assess the impact of the two different model structures on forecast

  3. Visually Comparing Weather Features in Forecasts.

    Science.gov (United States)

    Quinan, P Samuel; Meyer, Miriah

    2016-01-01

    Meteorologists process and analyze weather forecasts using visualization in order to examine the behaviors of and relationships among weather features. In this design study conducted with meteorologists in decision support roles, we identified and attempted to address two significant common challenges in weather visualization: the employment of inconsistent and often ineffective visual encoding practices across a wide range of visualizations, and a lack of support for directly visualizing how different weather features relate across an ensemble of possible forecast outcomes. In this work, we present a characterization of the problems and data associated with meteorological forecasting, we propose a set of informed default encoding choices that integrate existing meteorological conventions with effective visualization practice, and we extend a set of techniques as an initial step toward directly visualizing the interactions of multiple features over an ensemble forecast. We discuss the integration of these contributions into a functional prototype tool, and also reflect on the many practical challenges that arise when working with weather data.

  4. Seasonal time series forecasting: a comparative study of arima and ...

    African Journals Online (AJOL)

    This paper addresses the concerns of Faraway and Chatfield (1998) who questioned the forecasting ability of Artificial Neural Networks (ANN). In particular the paper compares the performance of Artificial Neural Networks (ANN) and ARIMA models in forecasting of seasonal (monthly) Time series. Using the Airline data ...

  5. Precipitation forecasting through an analog sorting technique: a comparative study

    Science.gov (United States)

    Ben Daoud, A.; Sauquet, E.; Lang, M.; Bontron, G.; Obled, C.

    2011-04-01

    This study aims at comparing two quantitative precipitation forecasting techniques based on the meteorological analogy concept. Method A considers first a selection of analogous situations at synoptic scale. Second a subset of the most similar situations in terms of hygrometry is extracted. Method B extends method A by two innovative ways, which are restricting the search for analogues with temperature information instead of the common season criterion, and exploiting the information about vertical motion considering vertical velocity. Forecasts are evaluated in a perfect prognosis context and in operational conditions as well, by mean of verification measures (Continuous Ranked Probability Skill Score and scores computed from contingency tables). Results of the case study in France show that: (1) there is an increase in forecast skill when temperature and vertical velocity are included in the procedure, (2) it is possible to anticipate rainfall events up to one week ahead and (3) the introduction of new variables such as vertical velocity may be useless beyond few days ahead if the forecast of the weather model is not reliable.

  6. COMPARATIVE STUDY OF PEDAGOGICAL FORECASTING GENESIS IN RUSSIAN AND POLISH PEDAGOGICAL EDUCATION

    Directory of Open Access Journals (Sweden)

    Alla Fedorovna Matuszak (Prisyazhnaya

    2016-02-01

    Full Text Available The paper describes the genesis of pedagogical forecasting in Russia and Poland in the context of the development of general forecasting theory mainly with the emphasis on pedagogical education. The periods of forecasting theory development (implicit, explicit, institutional, global are presented. The content of each period viewing Russian and Polish education is described. In the implicit period the first attempts are made to predict educational issues. During the explicit period pedagogical forecasting obtains methodology of research. In the institutional period pedagogical forecasting from the sphere of state planning becomes the activity necessary for all the participants of teaching and learning. The global period envi-sages implementing pedagogical forecasting theory to fulfill the tasks of modern education, e.g. doing international projects. The tasks of pedagogical forecasting in the global world include further convergence of educational systems of various countries.The purpose is to determine and describe the henesis of pedagogical forecasting in Russia and Polish pedagogical education in the context of the development of general forecasting theory.Methodology. To identify the periods theoretical methods (historical and analytical me-thod, comparative analysis, theoretical and methodological analysis, synthesis, generalization are used.The results of the research include specifying the periods of pedagogical prognostication as well as the tasks of pedagogical forecasting in the global world in Russian and Polish Universities.Practical implications of the research are of interest to educators of all levels, especially the ones working in higher pedagogical education.

  7. Wind Speed Forecasting by Wavelet Neural Networks: A Comparative Study

    Directory of Open Access Journals (Sweden)

    Chuanan Yao

    2013-01-01

    Full Text Available Due to the environmental degradation and depletion of conventional energy, much attention has been devoted to wind energy in many countries. The intermittent nature of wind power has had a great impact on power grid security. Accurate forecasting of wind speed plays a vital role in power system stability. This paper presents a comparison of three wavelet neural networks for short-term forecasting of wind speed. The first two combined models are two types of basic combinations of wavelet transform and neural network, namely, compact wavelet neural network (CWNN and loose wavelet neural network (LWNN in this study, and the third model is a new hybrid method based on the CWNN and LWNN models. The efficiency of the combined models has been evaluated by using actual wind speed from two test stations in North China. The results show that the forecasting performances of the CWNN and LWNN models are unstable and are affected by the test stations selected; the third model is far more accurate than the other forecasting models in spite of the drawback of lower computational efficiency.

  8. Comparative assessment of several post-processing methods for correcting evapotranspiration forecasts derived from TIGGE datasets.

    Science.gov (United States)

    Tian, D.; Medina, H.

    2017-12-01

    Post-processing of medium range reference evapotranspiration (ETo) forecasts based on numerical weather prediction (NWP) models has the potential of improving the quality and utility of these forecasts. This work compares the performance of several post-processing methods for correcting ETo forecasts over the continental U.S. generated from The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) database using data from Europe (EC), the United Kingdom (MO), and the United States (NCEP). The pondered post-processing techniques are: simple bias correction, the use of multimodels, the Ensemble Model Output Statistics (EMOS, Gneitting et al., 2005) and the Bayesian Model Averaging (BMA, Raftery et al., 2005). ETo estimates based on quality-controlled U.S. Regional Climate Reference Network measurements, and computed with the FAO 56 Penman Monteith equation, are adopted as baseline. EMOS and BMA are generally the most efficient post-processing techniques of the ETo forecasts. Nevertheless, the simple bias correction of the best model is commonly much more rewarding than using multimodel raw forecasts. Our results demonstrate the potential of different forecasting and post-processing frameworks in operational evapotranspiration and irrigation advisory systems at national scale.

  9. Financial impact of errors in business forecasting: a comparative study of linear models and neural networks

    Directory of Open Access Journals (Sweden)

    Claudimar Pereira da Veiga

    2012-08-01

    Full Text Available The importance of demand forecasting as a management tool is a well documented issue. However, it is difficult to measure costs generated by forecasting errors and to find a model that assimilate the detailed operation of each company adequately. In general, when linear models fail in the forecasting process, more complex nonlinear models are considered. Although some studies comparing traditional models and neural networks have been conducted in the literature, the conclusions are usually contradictory. In this sense, the objective was to compare the accuracy of linear methods and neural networks with the current method used by the company. The results of this analysis also served as input to evaluate influence of errors in demand forecasting on the financial performance of the company. The study was based on historical data from five groups of food products, from 2004 to 2008. In general, one can affirm that all models tested presented good results (much better than the current forecasting method used, with mean absolute percent error (MAPE around 10%. The total financial impact for the company was 6,05% on annual sales.

  10. Apixaban compared with warfarin in patients with atrial fibrillation and previous stroke or transient ischaemic attack

    DEFF Research Database (Denmark)

    Easton, J Donald; Lopes, Renato D; Bahit, M Cecilia

    2012-01-01

    In the ARISTOTLE trial, the rate of stroke or systemic embolism was reduced by apixaban compared with warfarin in patients with atrial fibrillation (AF). Patients with AF and previous stroke or transient ischaemic attack (TIA) have a high risk of stroke. We therefore aimed to assess the efficacy...

  11. A comparative verification of high resolution precipitation forecasts using model output statistics

    Science.gov (United States)

    van der Plas, Emiel; Schmeits, Maurice; Hooijman, Nicolien; Kok, Kees

    2017-04-01

    Verification of localized events such as precipitation has become even more challenging with the advent of high-resolution meso-scale numerical weather prediction (NWP). The realism of a forecast suggests that it should compare well against precipitation radar imagery with similar resolution, both spatially and temporally. Spatial verification methods solve some of the representativity issues that point verification gives rise to. In this study a verification strategy based on model output statistics is applied that aims to address both double penalty and resolution effects that are inherent to comparisons of NWP models with different resolutions. Using predictors based on spatial precipitation patterns around a set of stations, an extended logistic regression (ELR) equation is deduced, leading to a probability forecast distribution of precipitation for each NWP model, analysis and lead time. The ELR equations are derived for predictands based on areal calibrated radar precipitation and SYNOP observations. The aim is to extract maximum information from a series of precipitation forecasts, like a trained forecaster would. The method is applied to the non-hydrostatic model Harmonie (2.5 km resolution), Hirlam (11 km resolution) and the ECMWF model (16 km resolution), overall yielding similar Brier skill scores for the 3 post-processed models, but larger differences for individual lead times. Besides, the Fractions Skill Score is computed using the 3 deterministic forecasts, showing somewhat better skill for the Harmonie model. In other words, despite the realism of Harmonie precipitation forecasts, they only perform similarly or somewhat better than precipitation forecasts from the 2 lower resolution models, at least in the Netherlands.

  12. Comparative Validation of Realtime Solar Wind Forecasting Using the UCSD Heliospheric Tomography Model

    Science.gov (United States)

    MacNeice, Peter; Taktakishvili, Alexandra; Jackson, Bernard; Clover, John; Bisi, Mario; Odstrcil, Dusan

    2011-01-01

    The University of California, San Diego 3D Heliospheric Tomography Model reconstructs the evolution of heliospheric structures, and can make forecasts of solar wind density and velocity up to 72 hours in the future. The latest model version, installed and running in realtime at the Community Coordinated Modeling Center(CCMC), analyzes scintillations of meter wavelength radio point sources recorded by the Solar-Terrestrial Environment Laboratory(STELab) together with realtime measurements of solar wind speed and density recorded by the Advanced Composition Explorer(ACE) Solar Wind Electron Proton Alpha Monitor(SWEPAM).The solution is reconstructed using tomographic techniques and a simple kinematic wind model. Since installation, the CCMC has been recording the model forecasts and comparing them with ACE measurements, and with forecasts made using other heliospheric models hosted by the CCMC. We report the preliminary results of this validation work and comparison with alternative models.

  13. Comparing the Selected Transfer Functions and Local Optimization Methods for Neural Network Flood Runoff Forecast

    Directory of Open Access Journals (Sweden)

    Petr Maca

    2014-01-01

    Full Text Available The presented paper aims to analyze the influence of the selection of transfer function and training algorithms on neural network flood runoff forecast. Nine of the most significant flood events, caused by the extreme rainfall, were selected from 10 years of measurement on small headwater catchment in the Czech Republic, and flood runoff forecast was investigated using the extensive set of multilayer perceptrons with one hidden layer of neurons. The analyzed artificial neural network models with 11 different activation functions in hidden layer were trained using 7 local optimization algorithms. The results show that the Levenberg-Marquardt algorithm was superior compared to the remaining tested local optimization methods. When comparing the 11 nonlinear transfer functions, used in hidden layer neurons, the RootSig function was superior compared to the rest of analyzed activation functions.

  14. A Comparative Study of Neural Networks and ANFIS for Forecasting Attendance Rate of Soccer Games

    Directory of Open Access Journals (Sweden)

    Mehmet Şahin

    2017-11-01

    Full Text Available The main purpose of this study was to develop and apply a neural network (NN approach and an adaptive neuro-fuzzy inference system (ANFIS model for forecasting the attendance rates at soccer games. The models were designed based on the characteristics of the problem. Past real data was used. Training data was used for training the models, and the testing data was used for evaluating the performance of the forecasting models. The obtained forecasting results were compared to the actual data and to each other. To evaluate the performance of the models, two statistical indicators, Mean Absolute Deviation (MAD and mean absolute percent error (MAPE, were used. Based on the results, the proposed neural network approach and the ANFIS model were shown to be effective in forecasting attendance at soccer games. The neural network approach performed better than the ANFIS model. The main contribution of this study is to introduce two effective techniques for estimating attendance at sports games. This is the first attempt to use an ANFIS model for that purpose.

  15. A COMPARATIVE STUDY OF FORECASTING MODELS FOR TREND AND SEASONAL TIME SERIES DOES COMPLEX MODEL ALWAYS YIELD BETTER FORECAST THAN SIMPLE MODELS

    Directory of Open Access Journals (Sweden)

    Suhartono Suhartono

    2005-01-01

    Full Text Available Many business and economic time series are non-stationary time series that contain trend and seasonal variations. Seasonality is a periodic and recurrent pattern caused by factors such as weather, holidays, or repeating promotions. A stochastic trend is often accompanied with the seasonal variations and can have a significant impact on various forecasting methods. In this paper, we will investigate and compare some forecasting methods for modeling time series with both trend and seasonal patterns. These methods are Winter's, Decomposition, Time Series Regression, ARIMA and Neural Networks models. In this empirical research, we study on the effectiveness of the forecasting performance, particularly to answer whether a complex method always give a better forecast than a simpler method. We use a real data, that is airline passenger data. The result shows that the more complex model does not always yield a better result than a simpler one. Additionally, we also find the possibility to do further research especially the use of hybrid model by combining some forecasting method to get better forecast, for example combination between decomposition (as data preprocessing and neural network model.

  16. Forecasting production of fossil fuel sources in Turkey using a comparative regression and ARIMA model

    International Nuclear Information System (INIS)

    Ediger, Volkan S.; Akar, Sertac; Ugurlu, Berkin

    2006-01-01

    This study aims at forecasting the most possible curve for domestic fossil fuel production of Turkey to help policy makers to develop policy implications for rapidly growing dependency problem on imported fossil fuels. The fossil fuel dependency problem is international in scope and context and Turkey is a typical example for emerging energy markets of the developing world. We developed a decision support system for forecasting fossil fuel production by applying a regression, ARIMA and SARIMA method to the historical data from 1950 to 2003 in a comparative manner. The method integrates each model by using some decision parameters related to goodness-of-fit and confidence interval, behavior of the curve, and reserves. Different forecasting models are proposed for different fossil fuel types. The best result is obtained for oil since the reserve classifications used it is much better defined them for the others. Our findings show that the fossil fuel production peak has already been reached; indicating the total fossil fuel production of the country will diminish and theoretically will end in 2038. However, production is expected to end in 2019 for hard coal, in 2024 for natural gas, in 2029 for oil and 2031 for asphaltite. The gap between the fossil fuel consumption and production is growing enormously and it reaches in 2030 to approximately twice of what it is in 2000

  17. Do emotional intelligence and previous caring experience influence student nurse performance? A comparative analysis.

    Science.gov (United States)

    Stenhouse, Rosie; Snowden, Austyn; Young, Jenny; Carver, Fiona; Carver, Hannah; Brown, Norrie

    2016-08-01

    Reports of poor nursing care have focused attention on values based selection of candidates onto nursing programmes. Values based selection lacks clarity and valid measures. Previous caring experience might lead to better care. Emotional intelligence (EI) might be associated with performance, is conceptualised and measurable. To examine the impact of 1) previous caring experience, 2) emotional intelligence 3) social connection scores on performance and retention in a cohort of first year nursing and midwifery students in Scotland. A longitudinal, quasi experimental design. Adult and mental health nursing, and midwifery programmes in a Scottish University. Adult, mental health and midwifery students (n=598) completed the Trait Emotional Intelligence Questionnaire-short form and Schutte's Emotional Intelligence Scale on entry to their programmes at a Scottish University, alongside demographic and previous caring experience data. Social connection was calculated from a subset of questions identified within the TEIQue-SF in a prior factor and Rasch analysis. Student performance was calculated as the mean mark across the year. Withdrawal data were gathered. 598 students completed baseline measures. 315 students declared previous caring experience, 277 not. An independent-samples t-test identified that those without previous caring experience scored higher on performance (57.33±11.38) than those with previous caring experience (54.87±11.19), a statistically significant difference of 2.47 (95% CI, 0.54 to 4.38), t(533)=2.52, p=.012. Emotional intelligence scores were not associated with performance. Social connection scores for those withdrawing (mean rank=249) and those remaining (mean rank=304.75) were statistically significantly different, U=15,300, z=-2.61, p$_amp_$lt;0.009. Previous caring experience led to worse performance in this cohort. Emotional intelligence was not a useful indicator of performance. Lower scores on the social connection factor were associated

  18. Diagnostics comparing sea surface temperature feedbacks from operational hurricane forecasts to observations

    Directory of Open Access Journals (Sweden)

    Ian D. Lloyd

    2011-11-01

    Full Text Available This paper examines the ability of recent versions of the Geophysical Fluid Dynamics Laboratory Operational Hurricane Forecast Model (GHM to reproduce the observed relationship between hurricane intensity and hurricane-induced Sea Surface Temperature (SST cooling. The analysis was performed by taking a Lagrangian composite of all hurricanes in the North Atlantic from 1998–2009 in observations and 2005–2009 for the GHM. A marked improvement in the intensity-SST relationship for the GHM compared to observations was found between the years 2005 and 2006–2009 due to the introduction of warm-core eddies, a representation of the loop current, and changes to the drag coefficient parameterization for bulk turbulent flux computation. A Conceptual Hurricane Intensity Model illustrates the essential steady-state characteristics of the intensity-SST relationship and is explained by two coupled equations for the atmosphere and ocean. The conceptual model qualitatively matches observations and the 2006–2009 period in the GHM, and presents supporting evidence for the conclusion that weaker upper oceanic thermal stratification in the Gulf of Mexico, caused by the introduction of the loop current and warm core eddies, is crucial to explaining the observed SST-intensity pattern. The diagnostics proposed by the conceptual model offer an independent set of metrics for comparing operational hurricane forecast models to observations.

  19. Comparative thermal cyclic test of different beryllium grades previously subjected to simulated disruption loads

    International Nuclear Information System (INIS)

    Gervash, A.; Giniyatulin, R.; Mazul, I.

    1999-01-01

    Considering beryllium as plasma facing armour this paper presents recent results obtained in Russia. A special process of joining beryllium to a Cu-alloy material structure is described and recent results of thermal cycling tests of such joints are presented. Summarizing the results, the authors show that a Cu-alloy heat sink structure armoured with beryllium can survive high heat fluxes (≥10 MW/m 2 ) during 1000 heating/cooling cycles without serious damage to the armour material and its joint. The principal feasibility of thermal cycling of beryllium grades and their joints directly in the core of a nuclear reactor is demonstrated and the main results of this test are presented. The paper also describes the thermal cycling of different beryllium grades having cracks initiated by previously applied high heat loads simulating plasma disruptions. (orig.)

  20. Rivaroxaban compared with warfarin in patients with atrial fibrillation and previous stroke or transient ischaemic attack

    DEFF Research Database (Denmark)

    Hankey, Graeme J; Patel, Manesh R; Stevens, Susanna R

    2012-01-01

    In ROCKET AF, rivaroxaban was non-inferior to adjusted-dose warfarin in preventing stroke or systemic embolism among patients with atrial fibrillation (AF). We aimed to investigate whether the efficacy and safety of rivaroxaban compared with warfarin is consistent among the subgroups of patients ...

  1. Comparative analysis of NOAA REFM and SNB3GEO tools for the forecast of the fluxes of high-energy electrons at GEO

    Science.gov (United States)

    Balikhin, M. A.; Rodriguez, J. V.; Boynton, R. J.; Walker, S. N.; Aryan, H.; Sibeck, D. G.; Billings, S. A.

    2016-01-01

    Reliable forecasts of relativistic electrons at geostationary orbit (GEO) are important for the mitigation of their hazardous effects on spacecraft at GEO. For a number of years the Space Weather Prediction Center at NOAA has provided advanced online forecasts of the fluence of electrons with energy >2 MeV at GEO using the Relativistic Electron Forecast Model (REFM). The REFM forecasts are based on real-time solar wind speed observations at L1. The high reliability of this forecasting tool serves as a benchmark for the assessment of other forecasting tools. Since 2012 the Sheffield SNB3GEO model has been operating online, providing a 24 h ahead forecast of the same fluxes. In addition to solar wind speed, the SNB3GEO forecasts use solar wind density and interplanetary magnetic field Bz observations at L1.The period of joint operation of both of these forecasts has been used to compare their accuracy. Daily averaged measurements of electron fluxes by GOES 13 have been used to estimate the prediction efficiency of both forecasting tools. To assess the reliability of both models to forecast infrequent events of very high fluxes, the Heidke skill score was employed. The results obtained indicate that SNB3GEO provides a more accurate 1 day ahead forecast when compared to REFM. It is shown that the correction methodology utilized by REFM potentially can improve the SNB3GEO forecast.

  2. Comparative Analysis of NOAA REFM and SNB3GEO Tools for the Forecast of the Fluxes of High-Energy Electrons at GEO

    Science.gov (United States)

    Balikhin, M. A.; Rodriguez, J. V.; Boynton, R. J.; Walker, S. N.; Aryan, Homayon; Sibeck, D. G.; Billings, S. A.

    2016-01-01

    Reliable forecasts of relativistic electrons at geostationary orbit (GEO) are important for the mitigation of their hazardous effects on spacecraft at GEO. For a number of years the Space Weather Prediction Center at NOAA has provided advanced online forecasts of the fluence of electrons with energy >2 MeV at GEO using the Relativistic Electron Forecast Model (REFM). The REFM forecasts are based on real-time solar wind speed observations at L1. The high reliability of this forecasting tool serves as a benchmark for the assessment of other forecasting tools. Since 2012 the Sheffield SNB3GEO model has been operating online, providing a 24 h ahead forecast of the same fluxes. In addition to solar wind speed, the SNB3GEO forecasts use solar wind density and interplanetary magnetic field B(sub z) observations at L1. The period of joint operation of both of these forecasts has been used to compare their accuracy. Daily averaged measurements of electron fluxes by GOES 13 have been used to estimate the prediction efficiency of both forecasting tools. To assess the reliability of both models to forecast infrequent events of very high fluxes, the Heidke skill score was employed. The results obtained indicate that SNB3GEO provides a more accurate 1 day ahead forecast when compared to REFM. It is shown that the correction methodology utilized by REFM potentially can improve the SNB3GEO forecast.

  3. Comparing Predictive Accuracy under Long Memory - With an Application to Volatility Forecasting

    DEFF Research Database (Denmark)

    Kruse, Robinson; Leschinski, Christian; Will, Michael

    This paper extends the popular Diebold-Mariano test to situations when the forecast error loss differential exhibits long memory. It is shown that this situation can arise frequently, since long memory can be transmitted from forecasts and the forecast objective to forecast error loss differentials....... The nature of this transmission mainly depends on the (un)biasedness of the forecasts and whether the involved series share common long memory. Further results show that the conventional Diebold-Mariano test is invalidated under these circumstances. Robust statistics based on a memory and autocorrelation...... consistent estimator and an extended fixed-bandwidth approach are considered. The subsequent Monte Carlo study provides a novel comparison of these robust statistics. As empirical applications, we conduct forecast comparison tests for the realized volatility of the Standard and Poors 500 index among recent...

  4. A comparative study on GM (1,1) and FRMGM (1,1) model in forecasting FBM KLCI

    Science.gov (United States)

    Ying, Sah Pei; Zakaria, Syerrina; Mutalib, Sharifah Sakinah Syed Abd

    2017-11-01

    FTSE Bursa Malaysia Kuala Lumpur Composite Index (FBM KLCI) is a group of indexes combined in a standardized way and is used to measure the Malaysia overall market across the time. Although composite index can give ideas about stock market to investors, it is hard to predict accurately because it is volatile and it is necessary to identify a best model to forecast FBM KLCI. The objective of this study is to determine the most accurate forecasting model between GM (1,1) model and Fourier Residual Modification GM (1,1) (FRMGM (1,1)) model to forecast FBM KLCI. In this study, the actual daily closing data of FBM KLCI was collected from January 1, 2016 to March 15, 2016. GM (1,1) model and FRMGM (1,1) model were used to build the grey model and to test forecasting power of both models. Mean Absolute Percentage Error (MAPE) was used as a measure to determine the best model. Forecasted value by FRMGM (1,1) model do not differ much than the actual value compare to GM (1,1) model for in-sample and out-sample data. Results from MAPE also show that FRMGM (1,1) model is lower than GM (1,1) model for in-sample and out-sample data. These results shown that FRMGM (1,1) model is better than GM (1,1) model to forecast FBM KLCI.

  5. Probabilistic forecasts of extreme local precipitation using HARMONIE predictors and comparing 3 different post-processing methods

    Science.gov (United States)

    Whan, Kirien; Schmeits, Maurice

    2017-04-01

    Statistical post-processing of deterministic weather forecasts allows production of the full forecast distribution, and thus probabilistic forecasts, to be derived from that deterministic model output. We focus on local extreme precipitation amounts, as these are one predictand used in the KNMI weather warning system. As such, the predictand is based on the maximum hourly calibrated radar precipitation in a 3x3 km2 area within 12 large regions covering The Netherlands in a 6-hour afternoon period in summer (12-18 UTC). We compare three statistical methods when post-processing output from the operational high-resolution forecast model at KNMI, HARMONIE. These methods are 1) extended logistic regression (ELR), 2) an ensemble model output statistics approach where the parameters of a zero-adjusted gamma (ZAGA) distribution depends on a set of covariates and 3) quantile random forests (QRF). The set of predictors used as covariates includes model precipitation and indices capturing a variety of processes associated with deep convection. We use stepwise selection to select predictors for ELR and ZAGA based on the AIC. Predictors and coefficients are selected in a cross-validation framework based one two-years of training data and the skill of the forecasts are assessed on one-year of test data. The inclusion of additional predictors results in more skilfull forecasts, as expected, particularly for higher precipitation thresholds and for forecasts using the QRF method. We also assess the value of using a time-lagged ensemble. Forecasts derived from ZAGA and QRF are generally more skilfull, as defined by the Brier Skill Score, than ELR and lower precipitation amounts are skillfully predicted.

  6. Comparing Machine Learning and Decision Making Approaches to Forecast Long Lead Monthly Rainfall: The City of Vancouver, Canada

    Directory of Open Access Journals (Sweden)

    Zahra Zahmatkesh

    2018-01-01

    Full Text Available Estimating maximum possible rainfall is of great value for flood prediction and protection, particularly for regions, such as Canada, where urban and fluvial floods from extreme rainfalls have been known to be a major concern. In this study, a methodology is proposed to forecast real-time rainfall (with one month lead time using different number of spatial inputs with different orders of lags. For this purpose, two types of models are used. The first one is a machine learning data driven-based model, which uses a set of hydrologic variables as inputs, and the second one is an empirical-statistical model that employs the multi-criteria decision analysis method for rainfall forecasting. The data driven model is built based on Artificial Neural Networks (ANNs, while the developed multi-criteria decision analysis model uses Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS approach. A comprehensive set of spatially varying climate variables, including geopotential height, sea surface temperature, sea level pressure, humidity, temperature and pressure with different orders of lags is collected to form input vectors for the forecast models. Then, a feature selection method is employed to identify the most appropriate predictors. Two sets of results from the developed models, i.e., maximum daily rainfall in each month (RMAX and cumulative value of rainfall for each month (RCU, are considered as the target variables for forecast purpose. The results from both modeling approaches are compared using a number of evaluation criteria such as Nash-Sutcliffe Efficiency (NSE. The proposed models are applied for rainfall forecasting for a coastal area in Western Canada: Vancouver, British Columbia. Results indicate although data driven models such as ANNs work well for the simulation purpose, developed TOPSIS model considerably outperforms ANNs for the rainfall forecasting. ANNs show acceptable simulation performance during the

  7. More grip on inventory control through improved forecasting: A comparative study at three companies

    NARCIS (Netherlands)

    van Wingerden, E.; Basten, Robertus Johannes Ida; Dekker, R; Rustenburg, W.D.

    2014-01-01

    Inventory control for parts with infrequent demands is difficult since forecasting their demand is problematic. Traditional forecasting methods, such as moving average and single exponential smoothing, are known not to suffice since they do not cope well with periods with zero demands. Croston type

  8. A Kolmogorov-Smirnov Based Test for Comparing the Predictive Accuracy of Two Sets of Forecasts

    Directory of Open Access Journals (Sweden)

    Hossein Hassani

    2015-08-01

    Full Text Available This paper introduces a complement statistical test for distinguishing between the predictive accuracy of two sets of forecasts. We propose a non-parametric test founded upon the principles of the Kolmogorov-Smirnov (KS test, referred to as the KS Predictive Accuracy (KSPA test. The KSPA test is able to serve two distinct purposes. Initially, the test seeks to determine whether there exists a statistically significant difference between the distribution of forecast errors, and secondly it exploits the principles of stochastic dominance to determine whether the forecasts with the lower error also reports a stochastically smaller error than forecasts from a competing model, and thereby enables distinguishing between the predictive accuracy of forecasts. We perform a simulation study for the size and power of the proposed test and report the results for different noise distributions, sample sizes and forecasting horizons. The simulation results indicate that the KSPA test is correctly sized, and robust in the face of varying forecasting horizons and sample sizes along with significant accuracy gains reported especially in the case of small sample sizes. Real world applications are also considered to illustrate the applicability of the proposed KSPA test in practice.

  9. Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting

    Directory of Open Access Journals (Sweden)

    Xuejiao Ma

    2016-08-01

    Full Text Available Big data mining, analysis, and forecasting play vital roles in modern economic and industrial fields, especially in the energy system. Inaccurate forecasting may cause wastes of scarce energy or electricity shortages. However, forecasting in the energy system has proven to be a challenging task due to various unstable factors, such as high fluctuations, autocorrelation and stochastic volatility. To forecast time series data by using hybrid models is a feasible alternative of conventional single forecasting modelling approaches. This paper develops a group of hybrid models to solve the problems above by eliminating the noise in the original data sequence and optimizing the parameters in a back propagation neural network. One of contributions of this paper is to integrate the existing algorithms and models, which jointly show advances over the present state of the art. The results of comparative studies demonstrate that the hybrid models proposed not only satisfactorily approximate the actual value but also can be an effective tool in the planning and dispatching of smart grids.

  10. Open comparative trial of formestane versus megestrol acetate in postmenopausal patients with advanced breast cancer previously treated with tamoxifen

    NARCIS (Netherlands)

    Freue, M; Kjaer, M; Boni, C; Joliver, J; Janicke, F; Willemse, PHB; Coombes, RC; Van Belle, S; Perez-Carrion, R; Zieschang, J; de Palacios, PI; Rose, C

    The aim of the trial was to compare efficacy and safety of the aromatase inhibitor formestane (250 mg i.m. given every 2 weeks) with the progestin megestrol acetate (160 mg administered orally once daily), as second-line therapy in postmenopausal patients with advanced breast cancer previously

  11. Reproductive outcomes in adolescents who had a previous birth or an induced abortion compared to adolescents' first pregnancies

    Directory of Open Access Journals (Sweden)

    Wenzlaff Paul

    2008-01-01

    Full Text Available Abstract Background Recently, attention has been focused on subsequent pregnancies among teenage mothers. Previous studies that compared the reproductive outcomes of teenage nulliparae and multiparae often did not consider the adolescents' reproductive histories. Thus, the authors compared the risks for adverse reproductive outcomes of adolescent nulliparae to teenagers who either have had an induced abortion or a previous birth. Methods In this retrospective cohort study we used perinatal data prospectively collected by obstetricians and midwives from 1990–1999 (participation rate 87–98% of all hospitals in Lower Saxony, Germany. From the 9742 eligible births among adolescents, women with multiple births, >1 previous pregnancies, or a previous spontaneous miscarriage were deleted and 8857 women Results In bivariate logistic regression analyses, compared to nulliparous teenagers, adolescents with a previous birth had higher risks for perinatal [OR = 2.08, CI = 1.11,3.89] and neonatal [OR = 4.31, CI = 1.77,10.52] mortality and adolescents with a previous abortion had higher risks for stillbirths [OR = 3.31, CI = 1.01,10.88] and preterm births [OR = 2.21, CI = 1.07,4.58]. After adjusting for maternal nationality, partner status, smoking, prenatal care and pre-pregnancy BMI, adolescents with a previous birth were at higher risk for perinatal [OR = 2.35, CI = 1.14,4.86] and neonatal mortality [OR = 4.70, CI = 1.60,13.81] and adolescents with a previous abortion had a higher risk for very low birthweight infants [OR = 2.74, CI = 1.06,7.09] than nulliparous teenagers. Conclusion The results suggest that teenagers who give birth twice as adolescents have worse outcomes in their second pregnancy compared to those teenagers who are giving birth for the first time. The prevention of the second pregnancy during adolescence is an important public health objective and should be addressed by health care providers who attend the first birth or the abortion

  12. Comparing univariate techniques for tender price index forecasting: Box-Jenkins and neural network model

    Directory of Open Access Journals (Sweden)

    Olalekan Oshodi

    2017-09-01

    Full Text Available The poor performance of projects is a recurring event in the construction sector. Information gleaned from literature shows that uncertainty in project cost is one of the significant causes of this problem. Reliable forecast of construction cost is useful in mitigating the adverse effect of its fluctuation, however the availability of data for the development of multivariate models for construction cost forecasting remains a challenge. The study seeks to investigate the reliability of using univariate models for tender price index forecasting. Box-Jenkins and neural network are the modelling techniques applied in this study. The results show that the neural network model outperforms the Box-Jenkins model, in terms of accuracy. In addition, the neural network model provides a reliable forecast of tender price index over a period of 12 quarters ahead. The limitations of using the univariate models are elaborated. The developed neural network model can be used by stakeholders as a tool for predicting the movements in tender price index. In addition, the univariate models developed in the present study are particularly useful in countries where limited data reduces the possibility of applying multivariate models.

  13. Reference values for spirometry, including vital capacity, in Japanese adults calculated with the LMS method and compared with previous values.

    Science.gov (United States)

    Kubota, Masaru; Kobayashi, Hirosuke; Quanjer, Philip H; Omori, Hisamitsu; Tatsumi, Koichiro; Kanazawa, Minoru

    2014-07-01

    Reference values for lung function tests should be periodically updated because of birth cohort effects and improved technology. This study updates the spirometric reference values, including vital capacity (VC), for Japanese adults and compares the new reference values with previous Japanese reference values. Spirometric data from healthy non-smokers (20,341 individuals aged 17-95 years, 67% females) were collected from 12 centers across Japan, and reference equations were derived using the LMS method. This method incorporates modeling skewness (lambda: L), mean (mu: M), and coefficient of variation (sigma: S), which are functions of sex, age, and height. In addition, the age-specific lower limits of normal (LLN) were calculated. Spirometric reference values for the 17-95-year age range and the age-dependent LLN for Japanese adults were derived. The new reference values for FEV(1) in males are smaller, while those for VC and FVC in middle age and elderly males and those for FEV(1), VC, and FVC in females are larger than the previous values. The LLN of the FEV(1)/FVC for females is larger than previous values. The FVC is significantly smaller than the VC in the elderly. The new reference values faithfully reflect spirometric indices and provide an age-specific LLN for the 17-95-year age range, enabling improved diagnostic accuracy. Compared with previous prediction equations, they more accurately reflect the transition in pulmonary function during young adulthood. In elderly subjects, the FVC reference values are not interchangeable with the VC values. Copyright © 2014 The Japanese Respiratory Society. Published by Elsevier B.V. All rights reserved.

  14. At what price? A cost-effectiveness analysis comparing trial of labour after previous Caesarean versus elective repeat Caesarean delivery.

    LENUS (Irish Health Repository)

    Fawsitt, Christopher G

    2013-01-01

    Elective repeat caesarean delivery (ERCD) rates have been increasing worldwide, thus prompting obstetric discourse on the risks and benefits for the mother and infant. Yet, these increasing rates also have major economic implications for the health care system. Given the dearth of information on the cost-effectiveness related to mode of delivery, the aim of this paper was to perform an economic evaluation on the costs and short-term maternal health consequences associated with a trial of labour after one previous caesarean delivery compared with ERCD for low risk women in Ireland.

  15. Antimicrobial susceptibility of 6 antimicrobial agents in Helicobacter pylori clinical isolates by using EUCAST breakpoints compared with previously used breakpoints.

    Science.gov (United States)

    Alarcón, Teresa; Urruzuno, Pedro; Martínez, Maria Josefa; Domingo, Diego; Llorca, Laura; Correa, Ana; López-Brea, Manuel

    2017-05-01

    The aim of this study was to determine the differences in percentage resistance in H. pylori clinical isolates using EUCAST breakpoints compared with previously used breakpoints. MIC value distribution in H. pylori clinical isolates was also studied. Susceptibility to amoxicillin, tetracycline, metronidazole, clarithromycin, rifampicin and levofloxacin was performed by E-test in 824 H. pylori clinical isolates. EUCAST and previous breakpoints defined resistance as follows: MIC >0.12mg/L and ≥2mg/L for amoxicillin, >8mg/L and ≥8mg/L for metronidazole, >0.5mg/L and ≥1mg/L for clarithromycin, >1mg/L and ≥32mg/L for rifampicin, and >1mg/L and ≥4mg/L for tetracycline and >1mg/L levofloxacin. Overall resistance rate by EUCAST and by previous breakpoints was 8.5% and 3.2% for amoxicillin, 0.6% and 0.1% for tetracycline, 39.2% and 39.7% for metronidazole, 51.2% and 51.2% for clarithromycin, 32% and 3.1% for rifampicin, and 6.7% and 6.7% for levofloxacin. When using the different breakpoints for antimicrobial susceptibility testing, similar results were found with most antibiotics tested (tetracycline, metronidazole, clarithromycin, and levofloxacin), except for amoxicillin and rifampicin. Copyright © 2016 Elsevier España, S.L.U. and Sociedad Española de Enfermedades Infecciosas y Microbiología Clínica. All rights reserved.

  16. Municipal water consumption forecast accuracy

    Science.gov (United States)

    Fullerton, Thomas M.; Molina, Angel L.

    2010-06-01

    Municipal water consumption planning is an active area of research because of infrastructure construction and maintenance costs, supply constraints, and water quality assurance. In spite of that, relatively few water forecast accuracy assessments have been completed to date, although some internal documentation may exist as part of the proprietary "grey literature." This study utilizes a data set of previously published municipal consumption forecasts to partially fill that gap in the empirical water economics literature. Previously published municipal water econometric forecasts for three public utilities are examined for predictive accuracy against two random walk benchmarks commonly used in regional analyses. Descriptive metrics used to quantify forecast accuracy include root-mean-square error and Theil inequality statistics. Formal statistical assessments are completed using four-pronged error differential regression F tests. Similar to studies for other metropolitan econometric forecasts in areas with similar demographic and labor market characteristics, model predictive performances for the municipal water aggregates in this effort are mixed for each of the municipalities included in the sample. Given the competitiveness of the benchmarks, analysts should employ care when utilizing econometric forecasts of municipal water consumption for planning purposes, comparing them to recent historical observations and trends to insure reliability. Comparative results using data from other markets, including regions facing differing labor and demographic conditions, would also be helpful.

  17. Single-case effect size calculation: comparing regression and non-parametric approaches across previously published reading intervention data sets.

    Science.gov (United States)

    Ross, Sarah G; Begeny, John C

    2014-08-01

    Growing from demands for accountability and research-based practice in the field of education, there is recent focus on developing standards for the implementation and analysis of single-case designs. Effect size methods for single-case designs provide a useful way to discuss treatment magnitude in the context of individual intervention. Although a standard effect size methodology does not yet exist within single-case research, panel experts recently recommended pairing regression and non-parametric approaches when analyzing effect size data. This study compared two single-case effect size methods: the regression-based, Allison-MT method and the newer, non-parametric, Tau-U method. Using previously published research that measured the Words read Correct per Minute (WCPM) variable, these two methods were examined by comparing differences in overall effect size scores and rankings of intervention effect. Results indicated that the regression method produced significantly larger effect sizes than the non-parametric method, but the rankings of the effect size scores had a strong, positive relation. Implications of these findings for research and practice are discussed. Copyright © 2014 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

  18. At what price? A cost-effectiveness analysis comparing trial of labour after previous caesarean versus elective repeat caesarean delivery.

    Directory of Open Access Journals (Sweden)

    Christopher G Fawsitt

    Full Text Available BACKGROUND: Elective repeat caesarean delivery (ERCD rates have been increasing worldwide, thus prompting obstetric discourse on the risks and benefits for the mother and infant. Yet, these increasing rates also have major economic implications for the health care system. Given the dearth of information on the cost-effectiveness related to mode of delivery, the aim of this paper was to perform an economic evaluation on the costs and short-term maternal health consequences associated with a trial of labour after one previous caesarean delivery compared with ERCD for low risk women in Ireland. METHODS: Using a decision analytic model, a cost-effectiveness analysis (CEA was performed where the measure of health gain was quality-adjusted life years (QALYs over a six-week time horizon. A review of international literature was conducted to derive representative estimates of adverse maternal health outcomes following a trial of labour after caesarean (TOLAC and ERCD. Delivery/procedure costs derived from primary data collection and combined both "bottom-up" and "top-down" costing estimations. RESULTS: Maternal morbidities emerged in twice as many cases in the TOLAC group than the ERCD group. However, a TOLAC was found to be the most-effective method of delivery because it was substantially less expensive than ERCD (€ 1,835.06 versus € 4,039.87 per women, respectively, and QALYs were modestly higher (0.84 versus 0.70. Our findings were supported by probabilistic sensitivity analysis. CONCLUSIONS: Clinicians need to be well informed of the benefits and risks of TOLAC among low risk women. Ideally, clinician-patient discourse would address differences in length of hospital stay and postpartum recovery time. While it is premature advocate a policy of TOLAC across maternity units, the results of the study prompt further analysis and repeat iterations, encouraging future studies to synthesis previous research and new and relevant evidence under a single

  19. WITHDRAWAL OF PREVIOUS COMPLAINT. A COMPARISON OF THE OLD AND THE NEW CRIMINAL CODE. PROBLEMS OF COMPARATIVE LAW

    Directory of Open Access Journals (Sweden)

    Alin Sorin NICOLESCU

    2015-07-01

    Full Text Available In criminal law previous complaint has a double legal valence, material and procedural in nature, constituting a condition for criminal liability, but also a functional condition in cases expressly and limitatively provided by law, a consequence of criminal sanction condition. For certain offenses criminal law determines the initiation of the criminal complaint by the introduction of previous complaint by the injured party, without its absence being a question of removing criminal liability. From the perspective of criminal material law conditioning of the existence of previous complaint ,its lack and withdrawal, are regulated by art. 157 and 158 of the New Penal Code, with significant changes in relation to the old regulation of the institution . In terms of procedural aspect , previous complaint is regulated in art. 295-298 of the New Code of Criminal Procedure. Regarding the withdrawal of the previuos complaint, in the case of offenses for which the initiation of criminal proceedings is subject to the existence of such a complaint, we note that in the current Criminal Code this legal institution is regulated separately, representing both a cause for removal of criminal liability and a cause that preclude criminal action. This unilateral act of the will of the injured party - the withdrawal of the previous complaint, may be exercised only under certain conditions, namely: it can only be promoted in the case of the offenses for which the initiation of criminal proceedings is subject to the introduction of a previous complaint; it is made exclusively by the rightholder, by legal representatives or with the consent of the persons required by law for persons lacking legal capacity or having limited legal capacity;it must intervene until giving final judgment and it must represent an express and explicit manifestation. A novelty isrepresented by the possibility of withdrawing previous complaint if the prosecution was driven ex officio, although for

  20. The strategy of professional forecasting

    DEFF Research Database (Denmark)

    Ottaviani, Marco; Sørensen, Peter Norman

    2006-01-01

    We develop and compare two theories of professional forecasters’ strategic behavior. The first theory, reputational cheap talk, posits that forecasters endeavor to convince the market that they are well informed. The market evaluates their forecasting talent on the basis of the forecasts and the ......We develop and compare two theories of professional forecasters’ strategic behavior. The first theory, reputational cheap talk, posits that forecasters endeavor to convince the market that they are well informed. The market evaluates their forecasting talent on the basis of the forecasts...... and the realized state. If the market expects forecasters to report their posterior expectations honestly, then forecasts are shaded toward the prior mean. With correct market expectations, equilibrium forecasts are imprecise but not shaded. The second theory posits that forecasters compete in a forecasting...

  1. DeMand: A tool for evaluating and comparing device-level demand and supply forecast models

    DEFF Research Database (Denmark)

    Neupane, Bijay; Siksnys, Laurynas; Pedersen, Torben Bach

    2016-01-01

    Fine-grained device-level predictions of both shiftable and non-shiftable energy demand and supply is vital in order to take advantage of Demand Response (DR) for efficient utilization of Renewable Energy Sources. The selection of an effective device-level load forecast model is a challenging task......, mainly due to the diversity of the models and the lack of proper tools and datasets that can be used to validate them. In this paper, we introduce the DeMand system for fine-tuning, analyzing, and validating the device-level forecast models. The system offers several built-in device-level measurement...... datasets, forecast models, features, and errors measures, thus semi-automating most of the steps of the forecast model selection and validation process. This paper presents the architecture and data model of the DeMand system; and provides a use-case example on how one particular forecast model...

  2. Comparative study of a novel application of automated HR HPV assay and stability in a previously untested Preservative media.

    Science.gov (United States)

    Morel, Mike E; McBride, Simon E; Gomez, Maria P

    2017-12-01

    The suitability and stability of cervical cells in Novaprep media (NHQ) for certain HPV assays is unknown. We evaluated the accuracy of an automated HPV assay (Abbott RealTime HR HPV) for cervical cells prepared in NHQ and NHQ with a pre-treatment to mimic a worst case clinical use, compared to the assay manufacturers media; repeatability and reproducibility of HPV results and the stability of detectable HPV in NHQ over time compared to CE marked liquid based cytology preservatives. Cell lines were used to simulate patient samples. Cells stored in NHQ produced accurate, repeatable and reproducible results. Stability in NHQ was comparable to the best performing LBC, with at least 7 months' stability at 18-25°C, 2-8°C, -20°C and -80°C; and at least 3 months' stability at 40°C. Similar results were obtained for pre-treated NHQ except only 3.5 months' stability at 18-25°C. Cell line samples in all media and concentrations tested were detected appropriately by the assay. Based on this first stage validation analytical study, cervical cells stored in NHQ are suitable for the Realtime HPV assay. There should be no reservations for inclusion of NHQ in any further validation and clinical performance evaluation of this assay. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

  3. Why preferring parametric forecasting to nonparametric methods?

    Science.gov (United States)

    Jabot, Franck

    2015-05-07

    A recent series of papers by Charles T. Perretti and collaborators have shown that nonparametric forecasting methods can outperform parametric methods in noisy nonlinear systems. Such a situation can arise because of two main reasons: the instability of parametric inference procedures in chaotic systems which can lead to biased parameter estimates, and the discrepancy between the real system dynamics and the modeled one, a problem that Perretti and collaborators call "the true model myth". Should ecologists go on using the demanding parametric machinery when trying to forecast the dynamics of complex ecosystems? Or should they rely on the elegant nonparametric approach that appears so promising? It will be here argued that ecological forecasting based on parametric models presents two key comparative advantages over nonparametric approaches. First, the likelihood of parametric forecasting failure can be diagnosed thanks to simple Bayesian model checking procedures. Second, when parametric forecasting is diagnosed to be reliable, forecasting uncertainty can be estimated on virtual data generated with the fitted to data parametric model. In contrast, nonparametric techniques provide forecasts with unknown reliability. This argumentation is illustrated with the simple theta-logistic model that was previously used by Perretti and collaborators to make their point. It should convince ecologists to stick to standard parametric approaches, until methods have been developed to assess the reliability of nonparametric forecasting. Copyright © 2015 Elsevier Ltd. All rights reserved.

  4. Medium-term hydrologic forecasting in mountain basins using forecasting of a mesoscale numerical weather model

    Science.gov (United States)

    Castro Heredia, L. M.; Suarez, F. I.; Fernandez, B.; Maass, T.

    2016-12-01

    For forecasting of water resources, weather outputs provide a valuable source of information which is available online. Compared to traditional ground-based meteorological gauges, weather forecasts data offer spatially and temporally continuous data not yet evaluated and used in the forecasting of water resources in mountainous regions in Chile. Nevertheless, the use of this non-conventional data has been limited or null in developing countries, basically because of the spatial resolution, despite the high potential in the management of water resources. The adequate incorporation of these data in hydrological models requires its evaluation while taking into account the features of river basins in mountainous regions. This work presents an integrated forecasting system which represents a radical change in the way of making the streamflow forecasts in Chile, where the snowmelt forecast is the principal component of water resources management. The integrated system is composed of a physically based hydrological model, which is the prediction tool itself, together with a methodology for remote sensing data gathering that allows feed the hydrological model in real time, and meteorological forecasts from NCEP-CFSv2. Previous to incorporation of meteorological forecasts into the hydrological model, the weather outputs were evaluated and downscaling using statistical downscaling methods. The hydrological forecasts were evaluated in two mountain basins in Chile for a term of six months for the snowmelt period. In every month an assimilation process was performed, and the hydrological forecast was improved. Each month, the snow cover area (from remote sensing) and the streamflow observed were used to assimilate the model parameters in order to improve the next hydrological forecast using meteorological forecasts. The operation of the system in real time shows a good agreement between the streamflow and the snow cover area observed. The hydrological model and the weather

  5. Analysing UK real estate market forecast disagreement

    OpenAIRE

    McAllister, Patrick; Newell, G.; Matysiak, George

    2005-01-01

    Given the significance of forecasting in real estate investment decisions, this paper investigates forecast uncertainty and disagreement in real estate market forecasts. Using the Investment Property Forum (IPF) quarterly survey amongst UK independent real estate forecasters, these real estate forecasts are compared with actual real estate performance to assess a number of real estate forecasting issues in the UK over 1999-2004, including real estate forecast error, bias and consensus. The re...

  6. Long-term Comparative Outcomes of Carotid Artery Stenting Following Previous Carotid Endarterectomy vs De Novo Lesions.

    Science.gov (United States)

    Mousa, Albeir Y; AbuRahma, Ali F; Bozzay, Joseph; Broce, Mike; Kali, Maher; Yacoub, Michael; Stone, Patrick; Bates, Mark C

    2015-06-01

    To report the long-term outcomes of patients who underwent carotid artery stenting (CAS) for de novo carotid stenosis vs patients treated for restenosis after carotid endarterectomy (CEA). A retrospective review was conducted of all 385 patients (mean age 68.6±9.6 years; 231 men) who underwent 435 CAS procedures at a large tertiary care center between January 1999 and December 2013. For analysis, patients were stratified based on their lesion type [de novo (dn) vs post-CEA restenosis (res)] and subclassified by symptoms status [symptomatic (Sx) or asymptomatic (Asx)], creating 4 groups: (1) CAS-dn Asx, (2) CAS-dn Sx, (3) CAS-res Asx, and (4) CAS-res Sx. For the CAS-res group, the mean elapsed time from CEA to CAS was 72.4±63.6 months. Outcomes included target vessel reintervention (TVR) and in-stent restenosis (ISR), the latter defined by a carotid duplex ultrasound velocity >275 cm/s. The main indication for initial carotid angiography with possible revascularization was severe carotid stenosis (≥70%-99% on duplex) in both CAS-dn and CAS-res groups (83.6% vs 83.7%, p=0.999). There were no significant differences in the percentage of patients with postintervention residual stenosis (<30%; 100% each arm) or complications between CAS-res vs CAS-dn: in-hospital stroke (1.4% vs 1.8%, respectively), myocardial infarction (0.9% vs 0%), or death (0.9% vs 0%). Mean follow-up was 62.4±45.6 months (median 53.5, range 1-180). Average clinical/TVR follow-up was greater for the CAS-res group (71.9±48.6 months) compared with 53.3±40.5 months for the CAS-dn group (p<0.001). Across the 4 study groups, there were no differences in freedom from ISR (p=0.174) or TVR (p=0.856). Multivariate analysis found peripheral vascular disease (PVD) as the sole ISR independent predictor [hazard ratio (HR) 1.92, 95% confidence interval (CI) 1.03 to 3.62, p=0.041], while significant predictors for TVR were age <65 years at the time of the procedure (HR 2.55, 95% CI 1.05 to 6.18, p=0

  7. 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

  8. From short term power forecasting to nowcasting - Benefiting from meteorological forecasts and measurements

    Science.gov (United States)

    Mey, Britta; Braun, Axel; Good, Garrett; Vogt, Stephan; Wessel, Arne; Dobschinski, Jan

    2016-04-01

    Today, wind and solar power forecasts with time horizons from zero to about three hours are essential for the reliable grid and market integration of wind and solar energy. With respect to closure times of German intra-day markets, power forecasts with time horizons of about one to two hours and an update frequency of 15 minutes are required for final trading activities, reducing the uncertainty of the day-ahead forecast of the previous day. Regarding grid security aspects, grid operators utilize such forecasts to create continuous intra-day grid congestion forecasts. In addition to these preventive measures, wind and solar power become more and more important for the provision of ancillary services by wind and solar farm operators. This use case mainly requires power forecasts with time horizons of less than one hour. In general, forecasts with time horizons below three hours are investigated within the nowcasting research area. Nowcasting models are mainly based on current observations and extrapolation methods. With respect to wind and solar power forecasts with horizons of up to three hours, it has been shown in studies that real-time power measurements have the highest information content as compared to other potential model input parameters. We will present results from studies focusing on the benefit of meteorological data (forecasts and/or measurements) in the field of solar and wind power forecasts with time horizons of up to a few hours. Wind farm forecast errors are for example reduced by using numerical weather prediction (NWP) data in the wind power prediction model along with real-time wind farm power measurements. Furthermore, spatially distributed NWP data in combination with German total wind power measurements helped in the reduction of extreme forecast errors. By using global radiation forecasts as an input for wind power forecasts, forecast error during sunrise and sunset could be reduced. In the field of German total solar power, nowcasting

  9. The GOCF/AWAP system - forecasting temperature extremes

    International Nuclear Information System (INIS)

    Fawcett, Robert; Hume, Timothy

    2010-01-01

    Gridded hourly temperature forecasts from the Bureau of Meteorology's Gridded Operational Consensus Forecasting (GOCF) system are combined in real time with the Australian Water Availability Project (AWAP) gridded daily temperature analyses to produce gridded daily maximum and minimum temperature forecasts with lead times from one to five days. These forecasts are compared against the historical record of AWAP daily temperature analyses (1911 to present), to identify regions where record or near-record temperatures are predicted to occur. This paper describes the GOCF/AWAP system, showing how the daily maximum and minimum temperature forecasts are prepared from the hourly forecasts, and how they are bias-corrected in real time using the AWAP analyses, against which they are subsequently verified. Using monthly climatologies of long-term daily mean, standard deviation and all-time highest and lowest on record, derived forecast products (for both maximum and minimum temperature) include ordinary and standardised anomalies, 'forecast - highest on record' and 'forecast - lowest on record'. Compensation for the climatological variation across the country is achieved in these last two products, which provide the necessary guidance as to whether or not record-breaking temperatures are expected, by expressing the forecast departure from the previous record in both 0 C and standard deviations.

  10. Multistep Wind Speed Forecasting Based on Wavelet and Gaussian Processes

    Directory of Open Access Journals (Sweden)

    Niya Chen

    2013-01-01

    Full Text Available Accurate wind speed forecasts are necessary for the safety and economy of the renewable energy utilization. The wind speed forecasts can be obtained by statistical model based on historical data. In this paper, a novel W-GP model (wavelet decomposition based Gaussian process learning paradigm is proposed for short-term wind speed forecasting. The nonstationary and nonlinear original wind speed series is first decomposed into a set of better-behaved constitutive subseries by wavelet decomposition. Then these sub-series are forecasted respectively by GP method, and the forecast results are summed to formulate an ensemble forecast for original wind speed series. Therefore, the previous process which obtains wind speed forecast result is named W-GP model. Finally, the proposed model is applied to short-term forecasting of the mean hourly and daily wind speed for a wind farm located in southern China. The prediction results indicate that the proposed W-GP model, which achieves a mean 13.34% improvement in RMSE (Root Mean Square Error compared to persistence method for mean hourly data and a mean 7.71% improvement for mean daily wind speed data, shows the best forecasting accuracy among several forecasting models.

  11. Location specific forecasting of maximum and minimum ...

    Indian Academy of Sciences (India)

    . The global NWP models, though able to provide reasonably good short- to medium-range weather forecasts, have comparatively less skill in forecasting surface parameters. It is well known that NWP model forecasts contain systematic biases ...

  12. Efficacy and safety of apixaban compared with aspirin in patients who previously tried but failed treatment with vitamin K antagonists: results from the AVERROES trial

    NARCIS (Netherlands)

    Coppens, Michiel; Synhorst, David; Eikelboom, John W.; Yusuf, Salim; Shestakovska, Olga; Connolly, Stuart J.

    2014-01-01

    The AVERROES double-blinded, randomized trial demonstrated that apixaban reduces the risk of stroke or systemic embolism (SSE) by 55% compared with aspirin without an increase in major bleeding in patients with atrial fibrillation either who previously tried but failed vitamin K antagonists (VKA)

  13. Efficacy and safety of apixaban compared with aspirin in patients who previously tried but failed treatment with vitamin K antagonists: results from the AVERROES trial.

    Science.gov (United States)

    Coppens, Michiel; Synhorst, David; Eikelboom, John W; Yusuf, Salim; Shestakovska, Olga; Connolly, Stuart J

    2014-07-21

    The AVERROES double-blinded, randomized trial demonstrated that apixaban reduces the risk of stroke or systemic embolism (SSE) by 55% compared with aspirin without an increase in major bleeding in patients with atrial fibrillation either who previously tried but failed vitamin K antagonists (VKA) therapy or who were expected to be unsuitable for VKA therapy. In this pre-specified analysis, we explored the consistency of the results in the subgroup of patients who tried but failed VKA therapy. Of 5599 patients, 2216 (40%) had previously failed VKA treatment [main reasons: poor international normalized ratio (INR) control 42%, refusal 37%, bleeding on VKA 8%]. Compared with those expected to be unsuitable for VKA therapy, those who had previously failed were older, more often male, had higher body mass index, more likely to have moderate renal impairment and a history of stroke and less likely to have heart failure or to be medically undertreated. The effects of apixaban compared with aspirin were consistent in those who previously failed and those who were expected to be unsuitable, for both SSE (P interaction 0.13) and major bleeding (P interaction 0.74) and were also consistent among different subgroups of patients who had previously failed VKA therapy defined by reasons for unsuitability, age, sex, renal function, CHADS2 score, aspirin dose, duration, indication, and quality of INR control of prior VKA use. The efficacy and safety of apixaban compared with aspirin is consistent in subgroups of patients who have previously attempted but failed VKA therapy, irrespective of the reason for discontinuation. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2014. For permissions please email: journals.permissions@oup.com.

  14. Forecasting Skill

    Science.gov (United States)

    1981-01-01

    indicated that forecasting experience has little relationship to forecasting performance. In the latter three studies, neophyte forecasters became... Europe . Within a few months after a new commander was assigned, this unit’s performance rose to first place in the theater and remained there

  15. Mortality and cardiovascular risk associated with different insulin secretagogues compared with metformin in type 2 diabetes, with or without a previous myocardial infarction: a nationwide study

    DEFF Research Database (Denmark)

    Schramm, Tina Ken; Gislason, Gunnar Hilmar; Vaag, Allan

    2011-01-01

    Aims The impact of insulin secretagogues (ISs) on long-term major clinical outcomes in type 2 diabetes remains unclear. We examined mortality and cardiovascular risk associated with all available ISs compared with metformin in a nationwide study. Methods and results All Danish residents >20 years......, initiating single-agent ISs or metformin between 1997 and 2006 were followed for up to 9 years (median 3.3 years) by individual-level linkage of nationwide registers. All-cause mortality, cardiovascular mortality, and the composite of myocardial infarction (MI), stroke, and cardiovascular mortality...... associated with individual ISs were investigated in patients with or without previous MI by multivariable Cox proportional-hazard analyses including propensity analyses. A total of 107 806 subjects were included, of whom 9607 had previous MI. Compared with metformin, glimepiride (hazard ratios and 95...

  16. Forecasting daily patient volumes in the emergency department.

    Science.gov (United States)

    Jones, Spencer S; Thomas, Alun; Evans, R Scott; Welch, Shari J; Haug, Peter J; Snow, Gregory L

    2008-02-01

    Shifts in the supply of and demand for emergency department (ED) resources make the efficient allocation of ED resources increasingly important. Forecasting is a vital activity that guides decision-making in many areas of economic, industrial, and scientific planning, but has gained little traction in the health care industry. There are few studies that explore the use of forecasting methods to predict patient volumes in the ED. The goals of this study are to explore and evaluate the use of several statistical forecasting methods to predict daily ED patient volumes at three diverse hospital EDs and to compare the accuracy of these methods to the accuracy of a previously proposed forecasting method. Daily patient arrivals at three hospital EDs were collected for the period January 1, 2005, through March 31, 2007. The authors evaluated the use of seasonal autoregressive integrated moving average, time series regression, exponential smoothing, and artificial neural network models to forecast daily patient volumes at each facility. Forecasts were made for horizons ranging from 1 to 30 days in advance. The forecast accuracy achieved by the various forecasting methods was compared to the forecast accuracy achieved when using a benchmark forecasting method already available in the emergency medicine literature. All time series methods considered in this analysis provided improved in-sample model goodness of fit. However, post-sample analysis revealed that time series regression models that augment linear regression models by accounting for serial autocorrelation offered only small improvements in terms of post-sample forecast accuracy, relative to multiple linear regression models, while seasonal autoregressive integrated moving average, exponential smoothing, and artificial neural network forecasting models did not provide consistently accurate forecasts of daily ED volumes. This study confirms the widely held belief that daily demand for ED services is characterized by

  17. Apixaban compared with warfarin in patients with atrial fibrillation and previous stroke or transient ischaemic attack: a subgroup analysis of the ARISTOTLE trial.

    Science.gov (United States)

    Easton, J Donald; Lopes, Renato D; Bahit, M Cecilia; Wojdyla, Daniel M; Granger, Christopher B; Wallentin, Lars; Alings, Marco; Goto, Shinya; Lewis, Basil S; Rosenqvist, Mårten; Hanna, Michael; Mohan, Puneet; Alexander, John H; Diener, Hans-Christoph

    2012-06-01

    In the ARISTOTLE trial, the rate of stroke or systemic embolism was reduced by apixaban compared with warfarin in patients with atrial fibrillation (AF). Patients with AF and previous stroke or transient ischaemic attack (TIA) have a high risk of stroke. We therefore aimed to assess the efficacy and safety of apixaban compared with warfarin in prespecified subgroups of patients with and without previous stroke or TIA. Between Dec 19, 2006, and April 2, 2010, patients were enrolled in the ARISTOTLE trial at 1034 clinical sites in 39 countries. 18,201 patients with AF or atrial flutter were randomly assigned to receive apixaban 5 mg twice daily or warfarin (target international normalised ratio 2·0-3·0). The median duration of follow-up was 1·8 years (IQR 1·4-2·3). The primary efficacy outcome was stroke or systemic embolism, analysed by intention to treat. The primary safety outcome was major bleeding in the on-treatment population. All participants, investigators, and sponsors were masked to treatment assignments. In this subgroup analysis, we estimated event rates and used Cox models to compare outcomes in patients with and without previous stroke or TIA. The ARISTOTLE trial is registered with ClinicalTrials.gov, number NTC00412984. Of the trial population, 3436 (19%) had a previous stroke or TIA. In the subgroup of patients with previous stroke or TIA, the rate of stroke or systemic embolism was 2·46 per 100 patient-years of follow-up in the apixaban group and 3·24 in the warfarin group (hazard ratio [HR] 0·76, 95% CI 0·56 to 1·03); in the subgroup of patients without previous stroke or TIA, the rate of stroke or systemic embolism was 1·01 per 100 patient-years of follow-up with apixaban and 1·23 with warfarin (HR 0·82, 95% CI 0·65 to 1·03; p for interaction=0·71). The absolute reduction in the rate of stroke and systemic embolism with apixaban versus warfarin was 0·77 per 100 patient-years of follow-up (95% CI -0·08 to 1·63) in patients with

  18. Rivaroxaban compared with warfarin in patients with atrial fibrillation and previous stroke or transient ischaemic attack: a subgroup analysis of ROCKET AF.

    Science.gov (United States)

    Hankey, Graeme J; Patel, Manesh R; Stevens, Susanna R; Becker, Richard C; Breithardt, Günter; Carolei, Antonio; Diener, Hans-Christoph; Donnan, Geoffrey A; Halperin, Jonathan L; Mahaffey, Kenneth W; Mas, Jean-Louis; Massaro, Ayrton; Norrving, Bo; Nessel, Christopher C; Paolini, John F; Roine, Risto O; Singer, Daniel E; Wong, Lawrence; Califf, Robert M; Fox, Keith A A; Hacke, Werner

    2012-04-01

    In ROCKET AF, rivaroxaban was non-inferior to adjusted-dose warfarin in preventing stroke or systemic embolism among patients with atrial fibrillation (AF). We aimed to investigate whether the efficacy and safety of rivaroxaban compared with warfarin is consistent among the subgroups of patients with and without previous stroke or transient ischaemic attack (TIA). In ROCKET AF, patients with AF who were at increased risk of stroke were randomly assigned (1:1) in a double-blind manner to rivaroxaban 20 mg daily or adjusted dose warfarin (international normalised ratio 2·0-3·0). Patients and investigators were masked to treatment allocation. Between Dec 18, 2006, and June 17, 2009, 14 264 patients from 1178 centres in 45 countries were randomly assigned. The primary endpoint was the composite of stroke or non-CNS systemic embolism. In this substudy we assessed the interaction of the treatment effects of rivaroxaban and warfarin among patients with and without previous stroke or TIA. Efficacy analyses were by intention to treat and safety analyses were done in the on-treatment population. ROCKET AF is registered with ClinicalTrials.gov, number NCT00403767. 7468 (52%) patients had a previous stroke (n=4907) or TIA (n=2561) and 6796 (48%) had no previous stroke or TIA. The number of events per 100 person-years for the primary endpoint in patients treated with rivaroxaban compared with warfarin was consistent among patients with previous stroke or TIA (2·79% rivaroxaban vs 2·96% warfarin; hazard ratio [HR] 0·94, 95% CI 0·77-1·16) and those without (1·44%vs 1·88%; 0·77, 0·58-1·01; interaction p=0·23). The number of major and non-major clinically relevant bleeding events per 100 person-years in patients treated with rivaroxaban compared with warfarin was consistent among patients with previous stroke or TIA (13·31% rivaroxaban vs 13·87% warfarin; HR 0·96, 95% CI 0·87-1·07) and those without (16·69%vs 15·19%; 1·10, 0·99-1·21; interaction p=0·08

  19. 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

  20. Weather forecast

    CERN Document Server

    Courtier, P

    1994-02-07

    Weather prediction is performed using the numerical model of the atmosphere evolution.The evolution equations are derived from the Navier Stokes equation for the adiabatic part but the are very much complicated by the change of phase of water, the radiation porocess and the boundary layer.The technique used operationally is described. Weather prediction is an initial value problem and accurate initial conditions need to be specified. Due to the small number of observations available (105 ) as compared to the dimension of the model state variable (107),the problem is largely underdetermined. Techniques of optimal control and inverse problems are used and have been adapted to the large dimension of our problem. our problem.The at mosphere is a chaotic system; the implication for weather prediction is discussed. Ensemble prediction is used operationally and the technique for generating initial conditions which lead to a numerical divergence of the subsequent forecasts is described.

  1. Defensive Forecasting: How to Use Similarity to Make Forecasts That Pass Statistical Tests

    Science.gov (United States)

    Shafer, Glenn

    Defensive forecasting first identifies a betting strategy that succeeds if probabilistic forecasts are inaccurate and then makes forecasts that will defeat this strategy. Both the strategy and the forecasts are based on the similarity of the current situation to previous situations.

  2. GEOS-5 seasonal forecast system

    Science.gov (United States)

    Borovikov, Anna; Cullather, Richard; Kovach, Robin; Marshak, Jelena; Vernieres, Guillaume; Vikhliaev, Yury; Zhao, Bin; Li, Zhao

    2017-09-01

    Ensembles of numerical forecasts based on perturbed initial conditions have long been used to improve estimates of both weather and climate forecasts. The Goddard Earth Observing System (GEOS) Atmosphere-Ocean General Circulation Model, Version 5 (GEOS-5 AOGCM) Seasonal-to-Interannual Forecast System has been used routinely by the GMAO since 2008, the current version since 2012. A coupled reanalysis starting in 1980 provides the initial conditions for the 9-month experimental forecasts. Once a month, sea surface temperature from a suite of 11 ensemble forecasts is contributed to the North American Multi-Model Ensemble (NMME) consensus project, which compares and distributes seasonal forecasts of ENSO events. Since June 2013, GEOS-5 forecasts of the Arctic sea-ice distribution were provided to the Sea-Ice Outlook project. The seasonal forecast output data includes surface fields, atmospheric and ocean fields, as well as sea ice thickness and area, and soil moisture variables. The current paper aims to document the characteristics of the GEOS-5 seasonal forecast system and to highlight forecast biases and skills of selected variables (sea surface temperature, air temperature at 2 m, precipitation and sea ice extent) to be used as a benchmark for the future GMAO seasonal forecast systems and to facilitate comparison with other global seasonal forecast systems.

  3. A COMPARATIVE STUDY OF PERINATAL OUTCOME IN WOMEN WITH RECURRENT PREECLAMPSIA TO WOMEN WITH PRE-ECLAMPSIA WHO WERE NORMOTENSIVE DURING THEIR PREVIOUS PREGNANCIES

    Directory of Open Access Journals (Sweden)

    Beena Guhan

    2016-08-01

    Full Text Available BACKGROUND AND OBJECTIVE Pre-eclampsia is a systemic syndrome that occurs in 3-5% of the pregnant women and is the leading cause of maternal/neonatal morbidity and mortality. We designed a study to compare perinatal outcome in women with recurrent preeclampsia to women with women with pre-eclampsia who were normotensive during their previous pregnancies. MATERIALS AND METHODS This study was conducted in Dept. of Obstetrics and Gynaecology, Institute of Maternal and Child Health, Government Medical College, Kozhikode for a period of 1 year from March 2013 to February 2014. We conducted this study in multiparous women who developed pre-eclampsia in index pregnancy (n=110. Among these, women who had pre-eclampsia in previous pregnancies (n=55 were compared to those who remained normotensive during their prior pregnancies (n=55. Maternal and foetal variables were compared. Multivariate logistic analyses were performed to examine the impact of pre-eclampsia on foetal outcome, preterm delivery, IUGR, still births adjusted for confounding variables. RESULTS No statistical significant difference was observed between the 2 groups in terms of age, gravida, parity. Women who experienced recurrent pre-eclampsia were at elevated risk for early onset of pre-eclampsia, deranged laboratory investigations (Hepatic and Renal parameters, Coagulation Profile, higher chances of maternal complications, foetal mortality and morbidity (Statistically significant P <0.05. Foetal loss was higher in women with recurrent pre-eclampsia (14.5% than in women with pre-eclampsia who had normotensive pregnancy history (1.8%. CONCLUSION Women with recurrent pre-eclampsia had a higher rate of perinatal loss compared to women with pre-eclampsia who were normotensive in their prior pregnancies.

  4. Evolving forecasting classifications and applications in health forecasting

    Directory of Open Access Journals (Sweden)

    Soyiri IN

    2012-05-01

    Full Text Available Ireneous N Soyiri1,2, Daniel D Reidpath11Global Public Health, JCSMHS, MONASH University, Selangor, Malaysia; 2School of Public Health, University of Ghana, Legon, Accra, GhanaAbstract: Health forecasting forewarns the health community about future health situations and disease episodes so that health systems can better allocate resources and manage demand. The tools used for developing and measuring the accuracy and validity of health forecasts commonly are not defined although they are usually adapted forms of statistical procedures. This review identifies previous typologies used in classifying the forecasting methods commonly used in forecasting health conditions or situations. It then discusses the strengths and weaknesses of these methods and presents the choices available for measuring the accuracy of health-forecasting models, including a note on the discrepancies in the modes of validation.Keywords: health forecast, health data, electronic health records, accuracy, cross validation, method, strengths and limitations

  5. Prolonged release oxycodone and naloxone treatment counteracts opioid-induced constipation in patients with severe pain compared to previous analgesic treatment.

    Science.gov (United States)

    Koopmans-Klein, Gineke; Van Op den Bosch, Joeri; van Megen, Yvonne; Prenen, Hans; Huygen, Frank; Mancini, Isabelle

    2017-12-01

    Treatment with prolonged-release oxycodone/naloxone (PR OXN) has been shown to improve opioid induced constipation (OIC) in constipated patients. This publication reports on a real-life observational study investigating the efficacy of PR OXN with regard to bowel function in patients switching to PR OXN from WHO step 1, step 2 and step 3 opioids. Patients with chronic pain experiencing insufficient pain relief and/or unacceptable side effects were switched to PR OXN and monitored in this observational study with respect to efficacy regarding bowel function and efficacy regarding pain relief in comparison with previous analgesic therapy. A patient was considered a responder with respect to efficacy if this assessment was "slightly better", "better" or "much better" compared with previous therapy. Bowel function index, pain intensity, quality of life, laxative medication use, and safety analgesic were also evaluated. A total of 1338 patients (mean [SD] age 64.3 [14.9], 63% female) were observed for 43 [3-166] days (median [range]) during treatment with PR OXN. Overall response rate regarding bowel function efficacy was 82.5%. Patients with symptoms of constipation at study entry obtained a clinically relevant improvement of the bowel function index (BFI) within the first 2 weeks of PR OXN treatment. Non-constipated patients at study entry maintained normal bowel function despite switching to treatment with the opioid PR OXN. In conclusion, treatment with PR OXN results in a significant and clinically relevant improvement of bowel function. During the observation of the treatment with PR OXN patients reported an improvement of quality of life (QoL). More interestingly, non-constipated patients maintained a normal bowel function, showing prevention of constipation despite the use of an opioid.

  6. Strategic Forecasting

    DEFF Research Database (Denmark)

    Duus, Henrik Johannsen

    2016-01-01

    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......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...

  7. 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...

  8. Ontario demand forecast from January 2004 to December 2013

    International Nuclear Information System (INIS)

    2003-01-01

    This document examined the demand forecast for electricity on the Independent Market Operator (IMO)-controlled grid in Ontario for the period 2004-2013. It serves as an assessment tool to determine whether existing and proposed generation and transmission facilities in the province will be sufficient to meet future electricity needs. Changes in methodology have been made to allow for an hourly peak versus the previously reported 20-minute peak value. Actual data through to the end of October 2002 was used to re-estimate energy demand. Compared to other developed countries, the outlook for the Canadian economy is optimistic. In addition, the economic forecast is better than that which formed the basis of the last ten-year forecast. Energy demand in the median growth scenario is increasing at an annual rate of 1.1 per cent rather than 0.9 per cent for the forecasted period of 2003-2012. The combination of a higher growth rate and a higher starting point results in a 2010 forecast of 168 TWh. It is expected that peak demand will grow faster than in the previous forecast. Summer peak demand averaging an annual growth of 1.3 per cent is forecasted for the period 2003-2012, with winter peak demand averaging a growth of 0.8 per cent. Under normal weather conditions, the electricity system is expected to peak in the summer of 2005 due to the continued demand for cooling load. However, under an extreme weather scenario, the system is already summer peaking. The improved economic outlook and higher starting point resulted in a higher forecast for energy. The electricity system is expected to winter peak during the first years of the forecasted period. The heating load is not expected to experience rapid growth in the next few years. 15 tabs., 14 figs

  9. A Delphi forecast of technology in education

    Science.gov (United States)

    Robinson, B. E.

    1973-01-01

    The results are reported of a Delphi forecast of the utilization and social impacts of large-scale educational telecommunications technology. The focus is on both forecasting methodology and educational technology. The various methods of forecasting used by futurists are analyzed from the perspective of the most appropriate method for a prognosticator of educational technology, and review and critical analysis are presented of previous forecasts and studies. Graphic responses, summarized comments, and a scenario of education in 1990 are presented.

  10. How accurate are forecasts of costs of energy? A methodological contribution

    International Nuclear Information System (INIS)

    Siddons, Craig; Allan, Grant; McIntyre, Stuart

    2015-01-01

    Forecasts of the cost of energy are typically presented as point estimates; however forecasts are seldom accurate, which makes it important to understand the uncertainty around these point estimates. The scale of the differences between forecasts and outturns (i.e. contemporary estimates) of costs may have important implications for government decisions on the appropriate form (and level) of support, modelling energy scenarios or industry investment appraisal. This paper proposes a methodology to assess the accuracy of cost forecasts. We apply this to levelised costs of energy for different generation technologies due to the availability of comparable forecasts and contemporary estimates, however the same methodology could be applied to the components of levelised costs, such as capital costs. The estimated “forecast errors” capture the accuracy of previous forecasts and can provide objective bounds to the range around current forecasts for such costs. The results from applying this method are illustrated using publicly available data for on- and off-shore wind, Nuclear and CCGT technologies, revealing the possible scale of “forecast errors” for these technologies. - Highlights: • A methodology to assess the accuracy of forecasts of costs of energy is outlined. • Method applied to illustrative data for four electricity generation technologies. • Results give an objective basis for sensitivity analysis around point estimates.

  11. Forecasting Hurricane Tracks Using a Complex Adaptive System

    National Research Council Canada - National Science Library

    Lear, Matthew R

    2005-01-01

    Forecast hurricane tracks using a multi-model ensemble that consists of linearly combining the individual model forecasts have greatly reduced the average forecast errors when compared to individual...

  12. A Complex Adaptive System Approach to Forecasting Hurricane Tracks

    National Research Council Canada - National Science Library

    Lear, Matthew R

    2005-01-01

    Forecast hurricane tracks using a multi-model ensemble that consists of linearly combining the individual model forecasts have greatly reduced the average forecast errors when compared to individual...

  13. 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.

  14. 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...

  15. Forecasting the future of biodiversity

    DEFF Research Database (Denmark)

    Fitzpatrick, M. C.; Sanders, Nate; Ferrier, Simon

    2011-01-01

    , but their application to forecasting climate change impacts on biodiversity has been limited. Here we compare forecasts of changes in patterns of ant biodiversity in North America derived from ensembles of single-species models to those from a multi-species modeling approach, Generalized Dissimilarity Modeling (GDM...... climate change impacts on biodiversity....

  16. Nonlinear time series modeling and forecasting the seismic data of the Hindu Kush region

    Science.gov (United States)

    Khan, Muhammad Yousaf; Mittnik, Stefan

    2017-11-01

    In this study, we extended the application of linear and nonlinear time models in the field of earthquake seismology and examined the out-of-sample forecast accuracy of linear Autoregressive (AR), Autoregressive Conditional Duration (ACD), Self-Exciting Threshold Autoregressive (SETAR), Threshold Autoregressive (TAR), Logistic Smooth Transition Autoregressive (LSTAR), Additive Autoregressive (AAR), and Artificial Neural Network (ANN) models for seismic data of the Hindu Kush region. We also extended the previous studies by using Vector Autoregressive (VAR) and Threshold Vector Autoregressive (TVAR) models and compared their forecasting accuracy with linear AR model. Unlike previous studies that typically consider the threshold model specifications by using internal threshold variable, we specified these models with external transition variables and compared their out-of-sample forecasting performance with the linear benchmark AR model. The modeling results show that time series models used in the present study are capable of capturing the dynamic structure present in the seismic data. The point forecast results indicate that the AR model generally outperforms the nonlinear models. However, in some cases, threshold models with external threshold variables specification produce more accurate forecasts, indicating that specification of threshold time series models is of crucial importance. For raw seismic data, the ACD model does not show an improved out-of-sample forecasting performance over the linear AR model. The results indicate that the AR model is the best forecasting device to model and forecast the raw seismic data of the Hindu Kush region.

  17. 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)

  18. Nonlinear time series modeling and forecasting the seismic data of the Hindu Kush region

    Science.gov (United States)

    Khan, Muhammad Yousaf; Mittnik, Stefan

    2018-01-01

    In this study, we extended the application of linear and nonlinear time models in the field of earthquake seismology and examined the out-of-sample forecast accuracy of linear Autoregressive (AR), Autoregressive Conditional Duration (ACD), Self-Exciting Threshold Autoregressive (SETAR), Threshold Autoregressive (TAR), Logistic Smooth Transition Autoregressive (LSTAR), Additive Autoregressive (AAR), and Artificial Neural Network (ANN) models for seismic data of the Hindu Kush region. We also extended the previous studies by using Vector Autoregressive (VAR) and Threshold Vector Autoregressive (TVAR) models and compared their forecasting accuracy with linear AR model. Unlike previous studies that typically consider the threshold model specifications by using internal threshold variable, we specified these models with external transition variables and compared their out-of-sample forecasting performance with the linear benchmark AR model. The modeling results show that time series models used in the present study are capable of capturing the dynamic structure present in the seismic data. The point forecast results indicate that the AR model generally outperforms the nonlinear models. However, in some cases, threshold models with external threshold variables specification produce more accurate forecasts, indicating that specification of threshold time series models is of crucial importance. For raw seismic data, the ACD model does not show an improved out-of-sample forecasting performance over the linear AR model. The results indicate that the AR model is the best forecasting device to model and forecast the raw seismic data of the Hindu Kush region.

  19. A Comparative Investigation of the Previous and New Secondary History Curriculum: The Issues of the Definition of the Aims and Objectives and the Selection of Curriculum Content

    Science.gov (United States)

    Dinc, Erkan

    2011-01-01

    Discussions on history teaching in Turkey indicate that the previous versions of the history curriculum and the pedagogy of history in the country bear many problems and deficiencies. The problems of Turkish history curriculum mainly arise from the perspectives it takes and the selection of its content. Since 2003, there have been extensive…

  20. IEA Wind Task 36 Forecasting

    Science.gov (United States)

    Giebel, Gregor; Cline, Joel; Frank, Helmut; Shaw, Will; Pinson, Pierre; Hodge, Bri-Mathias; Kariniotakis, Georges; Sempreviva, Anna Maria; Draxl, Caroline

    2017-04-01

    Wind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The new International Energy Agency (IEA) Task on Wind Power Forecasting tries to organise international collaboration, among national weather centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, UK MetOffice, …) and operational forecaster and forecast users. The Task is divided in three work packages: Firstly, a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction model physics, but also widely distributed information on accessible datasets for verification. Secondly, we will be aiming at an international pre-standard (an IEA Recommended Practice) on benchmarking and comparing wind power forecasts, including probabilistic forecasts aiming at industry and forecasters alike. This WP will also organise benchmarks, in cooperation with the IEA Task WakeBench. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions, especially probabilistic ones. The Operating Agent is Gregor Giebel of DTU, Co-Operating Agent is Joel Cline of the US Department of Energy. Collaboration in the task is solicited from everyone interested in the forecasting business. We will collaborate with IEA Task 31 Wakebench, which developed the Windbench benchmarking platform, which this task will use for forecasting benchmarks. The task runs for three years, 2016-2018. Main deliverables are an up-to-date list of current projects and main project results, including datasets which can be used by researchers around the world to improve their own models, an IEA Recommended Practice on performance evaluation of probabilistic forecasts, a position paper regarding the use of probabilistic forecasts

  1. New interval forecast for stationary autoregressive models ...

    African Journals Online (AJOL)

    In this paper, we proposed a new forecasting interval for stationary Autoregressive, AR(p) models using the Akaike information criterion (AIC) function. Ordinarily, the AIC function is used to determine the order of an AR(p) process. In this study however, AIC forecast interval compared favorably with the theoretical forecast ...

  2. Forecasting nuclear power supply with Bayesian autoregression

    International Nuclear Information System (INIS)

    Beck, R.; Solow, J.L.

    1994-01-01

    We explore the possibility of forecasting the quarterly US generation of electricity from nuclear power using a Bayesian autoregression model. In terms of forecasting accuracy, this approach compares favorably with both the Department of Energy's current forecasting methodology and their more recent efforts using ARIMA models, and it is extremely easy and inexpensive to implement. (author)

  3. A New Reference for Wind Power Forecasting

    DEFF Research Database (Denmark)

    Nielsen, Torben Skov; Joensen, Alfred K.; Madsen, Henrik

    1998-01-01

    In recent years some research towards developing forecasting models for wind power or energy has been carried out. In order to evaluate the prediction ability of these models, the forecasts are usually compared with those of the persistence forecast model. As shown in this article, however...

  4. Phase III trial comparing vinflunine with docetaxel in second-line advanced non-small-cell lung cancer previously treated with platinum-containing chemotherapy

    DEFF Research Database (Denmark)

    Krzakowski, Maciej; Ramlau, Rodryg; Jassem, Jacek

    2010-01-01

    To compare vinflunine (VFL) to docetaxel in patients with stage IIIB/IV non-small-cell lung cancer (NSCLC) who have experienced treatment failure with first-line platinum-based chemotherapy.......To compare vinflunine (VFL) to docetaxel in patients with stage IIIB/IV non-small-cell lung cancer (NSCLC) who have experienced treatment failure with first-line platinum-based chemotherapy....

  5. Using inferred probabilities to measure the accuracy of imprecise forecasts

    Directory of Open Access Journals (Sweden)

    Paul Lehner

    2012-11-01

    Full Text Available Research on forecasting is effectively limited to forecasts that are expressed with clarity; which is to say that the forecasted event must be sufficiently well-defined so that it can be clearly resolved whether or not the event occurred and forecasts certainties are expressed as quantitative probabilities. When forecasts are expressed with clarity, then quantitative measures (scoring rules, calibration, discrimination, etc. can be used to measure forecast accuracy, which in turn can be used to measure the comparative accuracy of different forecasting methods. Unfortunately most real world forecasts are not expressed clearly. This lack of clarity extends to both the description of the forecast event and to the use of vague language to express forecast certainty. It is thus difficult to assess the accuracy of most real world forecasts, and consequently the accuracy the methods used to generate real world forecasts. This paper addresses this deficiency by presenting an approach to measuring the accuracy of imprecise real world forecasts using the same quantitative metrics routinely used to measure the accuracy of well-defined forecasts. To demonstrate applicability, the Inferred Probability Method is applied to measure the accuracy of forecasts in fourteen documents examining complex political domains. Key words: inferred probability, imputed probability, judgment-based forecasting, forecast accuracy, imprecise forecasts, political forecasting, verbal probability, probability calibration.

  6. Temperature trends and prediction skill in NMME seasonal forecasts

    Science.gov (United States)

    Krakauer, Nir Y.

    2017-04-01

    The North American Multi-Model Ensemble (NMME) provides hindcasts and real-time predictions for monthly mean climate fields at lead times of up to a year. These global climate model outputs can be useful in constructing improved seasonal forecasts. Here, several simple methods are developed and evaluated for forecasting monthly temperatures up to a year in advance based on either unweighted or weighted NMME outputs, and compared to previously developed statistical forecast methods that use only time series of past observations. It is found that the NMME-based methods produce forecast temperature probability distributions that are appropriately shifted toward the warm end of past experience and also show skill at representing interannual variability. NMME-based methods clearly outperformed purely statistical methods for forecasting temperatures over ocean, though over land this improvement is less clear over the evaluation period tested. The NMME seasonal forecasts may be particularly useful for giving early warning of heat waves, given their societal significance and higher conditional skill under those conditions.

  7. Seasonal Forecast Skill And Teleconnections Over East Africa

    Science.gov (United States)

    MacLeod, D.; Palmer, T.

    2017-12-01

    Many people living in East Africa are significantly exposed to risks arising from climate variability. The region experiences two rainy seasons and poor performance of either or both of these (such as seen recently in 2016/17) reduces agricultural productivity and threatens food security. In combination with other factors this can lead to famine. By utilizing seasonal climate forecasts, preparatory actions can be taken in order to mitigate the risks arising from such climate variability. As part of the project ForPAc: "Towards forecast-based preparedness action", we are working with humanitarian agencies in Kenya to build such early warning systems on subseasonal-to-seasonal timescales. Here, the seasonal predictability and forecast skill of the two East African rainy seasons will be presented. Results from the new ECMWF operational forecasting system SEAS5 will be shown and compared to the previous System 4. Analysis of a new 110 year long atmosphere-only simulation will also be discussed, demonstrating impacts of atmosphere-ocean coupling as well as putting operational forecast skill in a long-term context. Particular focus will be given to the model representation of teleconnections of seasonal climate with global sea surface temperatures; highlighting sources of forecast error and informing future model development.

  8. Forecasting Final Energy Consumption using the Centered Moving Average Method and Time Series Analysis

    Directory of Open Access Journals (Sweden)

    Janina POPEANGA

    2014-05-01

    Full Text Available The forecasting of energy consumption has become one of the major fields of research in recent years. Accurate energy demand forecasting is essential in energy system operations and planning. In this paper, we will describe a method to determine the information that is useful for a good forecasting. Further, we adopt the time series modeling approach to model final energy consumption in Romania using previous data of 2010 to 2013. This method is implemented using stored procedures, developed in Oracle PL/SQL programming language. Finally, the developed model is compared for goodness of fit to the historical data and forecasting accuracy, and results are encouraging, showing that the forecast model is in control and is working correctly.

  9. Threshold Forecasting of Electricity Market Clearing Prices in Volatile Electricity Markets

    Science.gov (United States)

    Janjani, Arya

    Forecasting the hourly electricity prices has been a topic of interest since the introduction of deregulation and competition in electricity markets. This thesis introduces the new idea of "threshold forecasting" or "classification" of market prices as a new approach to electricity price prediction, versus point forecasting of prices, where the exact values of future prices are determined. The motivation for this idea is that the accuracy level presented in the literature for point price forecasting methods is usually low and not all market participants need to know the exact values of prices. Support Vector Machines (SVMs) are used as the core classifier in this work, and two alternative classification models are proposed. The classification results are compared with those of other alternative classifiers as well as those obtained using point forecasting methods in the previous literature. These comparisons verify the effectiveness of the proposed models.

  10. Exploring the interactions between forecast accuracy, risk perception and perceived forecast reliability in reservoir operator's decision to use forecast

    Science.gov (United States)

    Shafiee-Jood, M.; Cai, X.

    2017-12-01

    Advances in streamflow forecasts at different time scales offer a promise for proactive flood management and improved risk management. Despite the huge potential, previous studies have found that water resources managers are often not willing to incorporate streamflow forecasts information in decisions making, particularly in risky situations. While low accuracy of forecasts information is often cited as the main reason, some studies have found that implementation of streamflow forecasts sometimes is impeded by institutional obstacles and behavioral factors (e.g., risk perception). In fact, a seminal study by O'Connor et al. (2005) found that risk perception is the strongest determinant of forecast use while managers' perception about forecast reliability is not significant. In this study, we aim to address this issue again. However, instead of using survey data and regression analysis, we develop a theoretical framework to assess the user-perceived value of streamflow forecasts. The framework includes a novel behavioral component which incorporates both risk perception and perceived forecast reliability. The framework is then used in a hypothetical problem where reservoir operator should react to probabilistic flood forecasts with different reliabilities. The framework will allow us to explore the interactions among risk perception and perceived forecast reliability, and among the behavioral components and information accuracy. The findings will provide insights to improve the usability of flood forecasts information through better communication and education.

  11. Response to antiretroviral therapy (ART): comparing women with previous use of zidovudine monotherapy (ZDVm) in pregnancy with ART naïve women.

    Science.gov (United States)

    Huntington, Susie; Thorne, Claire; Anderson, Jane; Newell, Marie-Louise; Taylor, Graham P; Pillay, Deenan; Hill, Teresa; Tookey, Pat; Sabin, Caroline

    2014-03-04

    Short-term zidovudine monotherapy (ZDVm) remains an option for some pregnant HIV-positive women not requiring treatment for their own health but may affect treatment responses once antiretroviral therapy (ART) is subsequently started. Data were obtained by linking two UK studies: the UK Collaborative HIV Cohort (UK CHIC) study and the National Study of HIV in Pregnancy and Childhood (NSHPC). Treatment responses were assessed for 2028 women initiating ART at least one year after HIV-diagnosis. Outcomes were compared using logistic regression, proportional hazards regression or linear regression. In adjusted analyses, ART-naïve (n = 1937) and ZDVm-experienced (n = 91) women had similar increases in CD4 count and a similar proportion achieving virological suppression; both groups had a low risk of AIDS. In this setting, antenatal ZDVm exposure did not adversely impact on outcomes once ART was initiated for the woman's health.

  12. Multiclass pesticide analysis in fruit-based baby food: A comparative study of sample preparation techniques previous to gas chromatography-mass spectrometry.

    Science.gov (United States)

    Petrarca, Mateus H; Fernandes, José O; Godoy, Helena T; Cunha, Sara C

    2016-12-01

    With the aim to develop a new gas chromatography-mass spectrometry method to analyze 24 pesticide residues in baby foods at the level imposed by established regulation two simple, rapid and environmental-friendly sample preparation techniques based on QuEChERS (quick, easy, cheap, effective, robust and safe) were compared - QuEChERS with dispersive liquid-liquid microextraction (DLLME) and QuEChERS with dispersive solid-phase extraction (d-SPE). Both sample preparation techniques achieved suitable performance criteria, including selectivity, linearity, acceptable recovery (70-120%) and precision (⩽20%). A higher enrichment factor was observed for DLLME and consequently better limits of detection and quantification were obtained. Nevertheless, d-SPE provided a more effective removal of matrix co-extractives from extracts than DLLME, which contributed to lower matrix effects. Twenty-two commercial fruit-based baby food samples were analyzed by the developed method, being procymidone detected in one sample at a level above the legal limit established by EU. Copyright © 2016 Elsevier Ltd. All rights reserved.

  13. Estimation of analysis and forecast error variances

    Directory of Open Access Journals (Sweden)

    Malaquias Peña

    2014-11-01

    Full Text Available Accurate estimates of error variances in numerical analyses and forecasts (i.e. difference between analysis or forecast fields and nature on the resolved scales are critical for the evaluation of forecasting systems, the tuning of data assimilation (DA systems and the proper initialisation of ensemble forecasts. Errors in observations and the difficulty in their estimation, the fact that estimates of analysis errors derived via DA schemes, are influenced by the same assumptions as those used to create the analysis fields themselves, and the presumed but unknown correlation between analysis and forecast errors make the problem difficult. In this paper, an approach is introduced for the unbiased estimation of analysis and forecast errors. The method is independent of any assumption or tuning parameter used in DA schemes. The method combines information from differences between forecast and analysis fields (‘perceived forecast errors’ with prior knowledge regarding the time evolution of (1 forecast error variance and (2 correlation between errors in analyses and forecasts. The quality of the error estimates, given the validity of the prior relationships, depends on the sample size of independent measurements of perceived errors. In a simulated forecast environment, the method is demonstrated to reproduce the true analysis and forecast error within predicted error bounds. The method is then applied to forecasts from four leading numerical weather prediction centres to assess the performance of their corresponding DA and modelling systems. Error variance estimates are qualitatively consistent with earlier studies regarding the performance of the forecast systems compared. The estimated correlation between forecast and analysis errors is found to be a useful diagnostic of the performance of observing and DA systems. In case of significant model-related errors, a methodology to decompose initial value and model-related forecast errors is also

  14. Revisiting chlorophyll extraction methods in biological soil crusts – methodology for determination of chlorophyll a and chlorophyll a + b as compared to previous methods

    Directory of Open Access Journals (Sweden)

    J. Caesar

    2018-03-01

    Full Text Available Chlorophyll concentrations of biological soil crust (biocrust samples are commonly determined to quantify the relevance of photosynthetically active organisms within these surface soil communities. Whereas chlorophyll extraction methods for freshwater algae and leaf tissues of vascular plants are well established, there is still some uncertainty regarding the optimal extraction method for biocrusts, where organism composition is highly variable and samples comprise major amounts of soil. In this study we analyzed the efficiency of two different chlorophyll extraction solvents, the effect of grinding the soil samples prior to the extraction procedure, and the impact of shaking as an intermediate step during extraction. The analyses were conducted on four different types of biocrusts. Our results show that for all biocrust types chlorophyll contents obtained with ethanol were significantly lower than those obtained using dimethyl sulfoxide (DMSO as a solvent. Grinding of biocrust samples prior to analysis caused a highly significant decrease in chlorophyll content for green algal lichen- and cyanolichen-dominated biocrusts, and a tendency towards lower values for moss- and algae-dominated biocrusts. Shaking of the samples after each extraction step had a significant positive effect on the chlorophyll content of green algal lichen- and cyanolichen-dominated biocrusts. Based on our results we confirm a DMSO-based chlorophyll extraction method without grinding pretreatment and suggest the addition of an intermediate shaking step for complete chlorophyll extraction (see Supplement S6 for detailed manual. Determination of a universal chlorophyll extraction method for biocrusts is essential for the inter-comparability of publications conducted across all continents.

  15. Load forecasting method considering temperature effect for distribution network

    Directory of Open Access Journals (Sweden)

    Meng Xiao Fang

    2016-01-01

    Full Text Available To improve the accuracy of load forecasting, the temperature factor was introduced into the load forecasting in this paper. This paper analyzed the characteristics of power load variation, and researched the rule of the load with the temperature change. Based on the linear regression analysis, the mathematical model of load forecasting was presented with considering the temperature effect, and the steps of load forecasting were given. Used MATLAB, the temperature regression coefficient was calculated. Using the load forecasting model, the full-day load forecasting and time-sharing load forecasting were carried out. By comparing and analyzing the forecast error, the results showed that the error of time-sharing load forecasting method was small in this paper. The forecasting method is an effective method to improve the accuracy of load forecasting.

  16. Short-term wind power combined forecasting based on error forecast correction

    International Nuclear Information System (INIS)

    Liang, Zhengtang; Liang, Jun; Wang, Chengfu; Dong, Xiaoming; Miao, Xiaofeng

    2016-01-01

    combined models. The simulation results show that: (1) the autocorrelation function of the one-step ahead forecast errors of support vector machine shows more significant tailing than those of the autoregressive and persistence models and the correlation relationships of the multi-step ahead forecast errors of support vector machine do significantly exist, but in the case of the autoregressive and persistence models, they do not; (2) the proposed combined models have significantly enhanced the short-term wind power forecasting accuracy in the three forecasting modes. In particular, the one-step ahead forecasting accuracies of combined models show little difference; for the continuous multi-step ahead forecasting, the improvements of the proposed combined models compared with a certain individual model increase with increasing prediction steps.

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    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. kfnt 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. kbpt 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. koun 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. 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...

  19. kspi 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. kclm 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. kipl 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. 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...

  3. 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...

  4. krdg 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. kbgm 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. 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...

  7. 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...

  8. 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...

  9. 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...

  10. 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...

  11. kcwa 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. pawg 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. kgfl 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. 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...

  15. 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...

  16. pabr 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. kewn 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. kipt 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. kpeq 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. kdug 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. klbt 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. kcys 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. khio 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. kflo 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. klaf 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. kmlu 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. kact 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. khob 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. ktcs 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. 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...

  11. kmgw 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. kryy 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. kgtf 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. kjax 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. ktvf 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. kfat 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. kink 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. kshv 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. pajn 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. kpna 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. ktph 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. ksux 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. kcon 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. kpnc 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. kgsp 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. kgpt 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. kgcn 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. 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...

  9. pagk 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. korf 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. kpsm 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. kcre 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. krsw 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. papg 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. 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...

  16. krdu 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. kluk 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. keed 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. kiwd 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. kttn 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. kagc 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. kbmi 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. kapn 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. kgon 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. An Operational Short-Term Forecasting System for Regional Hydropower Management

    Science.gov (United States)

    Gronewold, A.; Labuhn, K. A.; Calappi, T. J.; MacNeil, A.

    2017-12-01

    The Niagara River is the natural outlet of Lake Erie and drains four of the five Great lakes. The river is used to move commerce and is home to both sport fishing and tourism industries. It also provides nearly 5 million kilowatts of hydropower for approximately 3.9 million homes. Due to a complex international treaty and the necessity of balancing water needs for an extensive tourism industry, the power entities operating on the river require detailed and accurate short-term river flow forecasts to maximize power output. A new forecast system is being evaluated that takes advantage of several previously independent components including the NOAA Lake Erie operational Forecast System (LEOFS), a previously developed HEC-RAS model, input from the New York Power Authority(NYPA) and Ontario Power Generation (OPG) and lateral flow forecasts for some of the tributaries provided by the NOAA Northeast River Forecast Center (NERFC). The Corps of Engineers updated the HEC-RAS model of the upper Niagara River to use the output forcing from LEOFS and a planned Grass Island Pool elevation provided by the power entities. The entire system has been integrated at the NERFC; it will be run multiple times per day with results provided to the Niagara River Control Center operators. The new model helps improve discharge forecasts by better accounting for dynamic conditions on Lake Erie. LEOFS captures seiche events on the lake that are often several meters of displacement from still water level. These seiche events translate into flow spikes that HEC-RAS routes downstream. Knowledge of the peak arrival time helps improve operational decisions at the Grass Island Pool. This poster will compare and contrast results from the existing operational flow forecast and the new integrated LEOFS/HEC-RAS forecast. This additional model will supply the Niagara River Control Center operators with multiple forecasts of flow to help improve forecasting under a wider variety of conditions.

  6. 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

  7. A Wind Forecasting System for Energy Application

    Science.gov (United States)

    Courtney, Jennifer; Lynch, Peter; Sweeney, Conor

    2010-05-01

    probabilistic wind forecasts which will be invaluable in wind energy management. In brief, this method turns the ensemble forecasts into a calibrated predictive probability distribution. Each ensemble member is provided with a 'weight' determined by its relative predictive skill over a training period of around 30 days. Verification of data is carried out using observed wind data from operational wind farms. These are then compared to existing forecasts produced by ECMWF and Met Eireann in relation to skill scores. We are developing decision-making models to show the benefits achieved using the data produced by our wind energy forecasting system. An energy trading model will be developed, based on the rules currently used by the Single Electricity Market Operator for energy trading in Ireland. This trading model will illustrate the potential for financial savings by using the forecast data generated by this research.

  8. Improving Arctic sea ice edge forecasts by assimilating high horizontal resolution sea ice concentration data into the US Navy's ice forecast systems

    Science.gov (United States)

    Posey, P. G.; Metzger, E. J.; Wallcraft, A. J.; Hebert, D. A.; Allard, R. A.; Smedstad, O. M.; Phelps, M. W.; Fetterer, F.; Stewart, J. S.; Meier, W. N.; Helfrich, S. R.

    2015-08-01

    This study presents the improvement in ice edge error within the US Navy's operational sea ice forecast systems gained by assimilating high horizontal resolution satellite-derived ice concentration products. Since the late 1980's, the ice forecast systems have assimilated near real-time sea ice concentration derived from the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSMI and then SSMIS). The resolution of the satellite-derived product was approximately the same as the previous operational ice forecast system (25 km). As the sea ice forecast model resolution increased over time, the need for higher horizontal resolution observational data grew. In 2013, a new Navy sea ice forecast system (Arctic Cap Nowcast/Forecast System - ACNFS) went into operations with a horizontal resolution of ~ 3.5 km at the North Pole. A method of blending ice concentration observations from the Advanced Microwave Scanning Radiometer (AMSR2) along with a sea ice mask produced by the National Ice Center (NIC) has been developed, resulting in an ice concentration product with very high spatial resolution. In this study, ACNFS was initialized with this newly developed high resolution blended ice concentration product. The daily ice edge locations from model hindcast simulations were compared against independent observed ice edge locations. ACNFS initialized using the high resolution blended ice concentration data product decreased predicted ice edge location error compared to the operational system that only assimilated SSMIS data. A second evaluation assimilating the new blended sea ice concentration product into the pre-operational Navy Global Ocean Forecast System 3.1 also showed a substantial improvement in ice edge location over a system using the SSMIS sea ice concentration product alone. This paper describes the technique used to create the blended sea ice concentration product and the significant improvements in ice edge forecasting in both of the

  9. Advanced Cloud Forecasting for Solar Energy Production

    Science.gov (United States)

    Werth, D. W.; Parker, M. J.

    2017-12-01

    A power utility must decide days in advance how it will allocate projected loads among its various generating sources. If the latter includes solar plants, the utility must predict how much energy the plants will produce - any shortfall will have to be compensated for by purchasing power as it is needed, when it is more expensive. To avoid this, utilities often err on the side of caution and assume that a relatively small amount of solar energy will be available, and allocate correspondingly more load to coal-fired plants. If solar irradiance can be predicted more accurately, utilities can be more confident that the predicted solar energy will indeed be available when needed, and assign solar plants a larger share of the future load. Solar power production is increasing in the Southeast, but is often hampered by irregular cloud fields, especially during high-pressure periods when rapid afternoon thunderstorm development can occur during what was predicted to be a clear day. We are currently developing an analog forecasting system to predict solar irradiance at the surface at the Savannah River Site in South Carolina, with the goal of improving predictions of available solar energy. Analog forecasting is based on the assumption that similar initial conditions will lead to similar outcomes, and involves the use of an algorithm to look through the weather patterns of the past to identify previous conditions (the analogs) similar to those of today. For our application, we select three predictor variables - sea-level pressure, 700mb geopotential, and 700mb humidity. These fields for the current day are compared to those from past days, and a weighted combination of the differences (defined by a cost function) is used to select the five best analog days. The observed solar irradiance values subsequent to the dates of those analogs are then combined to represent the forecast for the next day. We will explain how we apply the analog process, and compare it to existing

  10. Failure Forecasting in Triaxially Stressed Sandstones

    Science.gov (United States)

    Crippen, A.; Bell, A. F.; Curtis, A.; Main, I. G.

    2017-12-01

    Precursory signals to fracturing events have been observed to follow power-law accelerations in spatial, temporal, and size distributions leading up to catastrophic failure. In previous studies this behavior was modeled using Voight's relation of a geophysical precursor in order to perform `hindcasts' by solving for failure onset time. However, performing this analysis in retrospect creates a bias, as we know an event happened, when it happened, and we can search data for precursors accordingly. We aim to remove this retrospective bias, thereby allowing us to make failure forecasts in real-time in a rock deformation laboratory. We triaxially compressed water-saturated 100 mm sandstone cores (Pc= 25MPa, Pp = 5MPa, σ = 1.0E-5 s-1) to the point of failure while monitoring strain rate, differential stress, AEs, and continuous waveform data. Here we compare the current `hindcast` methods on synthetic and our real laboratory data. We then apply these techniques to increasing fractions of the data sets to observe the evolution of the failure forecast time with precursory data. We discuss these results as well as our plan to mitigate false positives and minimize errors for real-time application. Real-time failure forecasting could revolutionize the field of hazard mitigation of brittle failure processes by allowing non-invasive monitoring of civil structures, volcanoes, and possibly fault zones.

  11. Modeled Forecasts of Dengue Fever in San Juan, Puerto Rico Using NASA Satellite Enhanced Weather Forecasts

    Science.gov (United States)

    Morin, C.; Quattrochi, D. A.; Zavodsky, B.; Case, J.

    2015-12-01

    Dengue fever (DF) is an important mosquito transmitted disease that is strongly influenced by meteorological and environmental conditions. Recent research has focused on forecasting DF case numbers based on meteorological data. However, these forecasting tools have generally relied on empirical models that require long DF time series to train. Additionally, their accuracy has been tested retrospectively, using past meteorological data. Consequently, the operational utility of the forecasts are still in question because the error associated with weather and climate forecasts are not reflected in the results. Using up-to-date weekly dengue case numbers for model parameterization and weather forecast data as meteorological input, we produced weekly forecasts of DF cases in San Juan, Puerto Rico. Each week, the past weeks' case counts were used to re-parameterize a process-based DF model driven with updated weather forecast data to generate forecasts of DF case numbers. Real-time weather forecast data was produced using the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) system enhanced using additional high-resolution NASA satellite data. This methodology was conducted in a weekly iterative process with each DF forecast being evaluated using county-level DF cases reported by the Puerto Rico Department of Health. The one week DF forecasts were accurate especially considering the two sources of model error. First, weather forecasts were sometimes inaccurate and generally produced lower than observed temperatures. Second, the DF model was often overly influenced by the previous weeks DF case numbers, though this phenomenon could be lessened by increasing the number of simulations included in the forecast. Although these results are promising, we would like to develop a methodology to produce longer range forecasts so that public health workers can better prepare for dengue epidemics.

  12. The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation

    DEFF Research Database (Denmark)

    Sperati, Simone; Alessandrini, Stefano; Pinson, Pierre

    2015-01-01

    A benchmarking exercise was organized within the framework of the European Action Weather Intelligence for Renewable Energies (“WIRE”) with the purpose of evaluating the performance of state of the art models for short-term renewable energy forecasting. The exercise consisted in forecasting...... the power output of two wind farms and two photovoltaic power plants, in order to compare the merits of forecasts based on different modeling approaches and input data. It was thus possible to obtain a better knowledge of the state of the art in both wind and solar power forecasting, with an overview...... and comparison of the principal and the novel approaches that are used today in the field, and to assess the evolution of forecast performance with respect to previous benchmarking exercises. The outcome of this exercise consisted then in proposing new challenges in the renewable power forecasting field...

  13. The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation

    Directory of Open Access Journals (Sweden)

    Simone Sperati

    2015-09-01

    Full Text Available A benchmarking exercise was organized within the framework of the European Action Weather Intelligence for Renewable Energies (“WIRE” with the purpose of evaluating the performance of state of the art models for short-term renewable energy forecasting. The exercise consisted in forecasting the power output of two wind farms and two photovoltaic power plants, in order to compare the merits of forecasts based on different modeling approaches and input data. It was thus possible to obtain a better knowledge of the state of the art in both wind and solar power forecasting, with an overview and comparison of the principal and the novel approaches that are used today in the field, and to assess the evolution of forecast performance with respect to previous benchmarking exercises. The outcome of this exercise consisted then in proposing new challenges in the renewable power forecasting field and identifying the main areas for improving accuracy in the future.

  14. Comparative study of holt-winters triples exponential smoothing and seasonal Arima: Forecasting short term seasonal car sales in South Africa

    Directory of Open Access Journals (Sweden)

    Katleho Daniel Makatjane

    2016-02-01

    Full Text Available In this paper, both Seasonal ARIMA and Holt-Winters models are developed to predict the monthly car sales in South Africa using data for the period of January 1994 to December 2013. The purpose of this study is to choose an optimal model suited for the sector. The three error metrics; mean absolute error, mean absolute percentage error and root mean square error were used in making such a choice. Upon realizing that the three forecast errors could not provide concrete basis to make conclusion, the power test was calculated for each model proving Holt-Winters to having about 0.3% more predictive power. Empirical results also indicate that Holt-Winters model produced more precise short-term seasonal forecasts. The findings also revealed a structural break in April 2009, implying that the car industry was significantly affected by the 2008 and 2009 US financial crisis

  15. 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.

  16. How rolling forecasting facilitates dynamic, agile planning.

    Science.gov (United States)

    Miller, Debra; Allen, Michael; Schnittger, Stephanie; Hackman, Theresa

    2013-11-01

    Rolling forecasting may be used to replace or supplement the annual budget process. The rolling forecast typically builds on the organization's strategic financial plan, focusing on the first three years of plan projections and comparing the strategic financial plan assumptions with the organization's expected trajectory. Leaders can then identify and respond to gaps between the rolling forecast and the strategic financial plan on an ongoing basis.

  17. PREVIOUS SECOND TRIMESTER ABORTION

    African Journals Online (AJOL)

    PNLC

    PREVIOUS SECOND TRIMESTER ABORTION: A risk factor for third trimester uterine rupture in three ... for accurate diagnosis of uterine rupture. KEY WORDS: Induced second trimester abortion - Previous uterine surgery - Uterine rupture. ..... scarred uterus during second trimester misoprostol- induced labour for a missed ...

  18. Empirical seasonal forecasts of the NAO

    Science.gov (United States)

    Sanchezgomez, E.; Ortizbevia, M.

    2003-04-01

    We present here seasonal forecasts of the North Atlantic Oscillation (NAO) issued from ocean predictors with an empirical procedure. The Singular Values Decomposition (SVD) of the cross-correlation matrix between predictor and predictand fields at the lag used for the forecast lead is at the core of the empirical model. The main predictor field are sea surface temperature anomalies, although sea ice cover anomalies are also used. Forecasts are issued in probabilistic form. The model is an improvement over a previous version (1), where Sea Level Pressure Anomalies were first forecast, and the NAO Index built from this forecast field. Both correlation skill between forecast and observed field, and number of forecasts that hit the correct NAO sign, are used to assess the forecast performance , usually above those values found in the case of forecasts issued assuming persistence. For certain seasons and/or leads, values of the skill are above the .7 usefulness treshold. References (1) SanchezGomez, E. and Ortiz Bevia M., 2002, Estimacion de la evolucion pluviometrica de la Espana Seca atendiendo a diversos pronosticos empiricos de la NAO, in 'El Agua y el Clima', Publicaciones de la AEC, Serie A, N 3, pp 63-73, Palma de Mallorca, Spain

  19. FORECASTING THE NUMBER OF SPORT TOURISM ARRIVALS IN SOUTHWEST BULGARIA

    Directory of Open Access Journals (Sweden)

    Preslav Mihaylov Dimitrov

    2016-12-01

    Full Text Available This paper presents an application of some forecasting methods concerning sport tourism arrivals in Southwest Bulgaria: linear trend forecasting, double exponential forecasting (Holt’s method, triple exponential forecasting (the Holt-Winters Method, and the ARIMA method. A specially designed model for estimating the weight coefficient needed for determining the size of the sport tourism’s sector in the time series of the available data and in the forecast values is presented. In order to test the forecasting methods and produce forecasts up to the year 2030, a time series and past period predictions have been constructed based on statistical records since 1964. Several major problems in the application of the exponential smoothing methods for the purpose of the long-run forecasting and the needs of the sport tourism subsector of Bulgaria tourism industry are addressed. These problems include (a finding a suitable general indicator, (b calculating short-term and long-term forecasts, (c comparing the results of the forecast techniques on the basis of the errors in the forecasts, (d estimating the size of the sport tourism in Southwest Bulgaria in certain terms so that the forecast(s of the above-mentioned general indicator could be particularized especially for examined sub-sector and region. The results from the different forecasting methods and techniques are presented and conclusions are drawn regarding the reliability of the forecasts.

  20. Assimilating high horizontal resolution sea ice concentration data into the US Navy's ice forecast systems: Arctic Cap Nowcast/Forecast System (ACNFS) and the Global Ocean Forecast System (GOFS 3.1)

    Science.gov (United States)

    Posey, P. G.; Metzger, E. J.; Wallcraft, A. J.; Hebert, D. A.; Allard, R. A.; Smedstad, O. M.; Phelps, M. W.; Fetterer, F.; Stewart, J. S.; Meier, W. N.; Helfrich, S. R.

    2015-04-01

    This study presents the improvement in the US Navy's operational sea ice forecast systems gained by assimilating high horizontal resolution satellite-derived ice concentration products. Since the late 1980's, the ice forecast systems have assimilated near real-time sea ice concentration derived from the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSMI and then SSMIS). The resolution of the satellite-derived product was approximately the same as the previous operational ice forecast system (25 km). As the sea ice forecast model resolution increased over time, the need for higher horizontal resolution observational data grew. In 2013, a new Navy sea ice forecast system (Arctic Cap Nowcast/Forecast System - ACNFS) went into operations with a horizontal resolution of ~3.5 km at the North Pole. A method of blending ice concentration observations from the Advanced Microwave Scanning Radiometer (AMSR2) along with a sea ice mask produced by the National Ice Center (NIC) has been developed resulting in an ice concentration product with very high spatial resolution. In this study, ACNFS was initialized with this newly developed high resolution blended ice concentration product. The daily ice edge locations from model hindcast simulations were compared against independent observed ice edge locations. ACNFS initialized using the high resolution blended ice concentration data product decreased predicted ice edge location error compared to the operational system that only assimilated SSMIS data. A second evaluation assimilating the new blended sea ice concentration product into the pre-operational Navy Global Ocean Forecast System 3.1 also showed a substantial improvement in ice edge location over a system using the SSMIS sea ice concentration product alone. This paper describes the technique used to create the blended sea ice concentration product and the significant improvements to both of the Navy's sea ice forecasting systems.

  1. Air Quality Forecasts Using the NASA GEOS Model

    Science.gov (United States)

    Keller, Christoph A.; Knowland, K. Emma; Nielsen, Jon E.; Orbe, Clara; Ott, Lesley; Pawson, Steven; Saunders, Emily; Duncan, Bryan; Follette-Cook, Melanie; Liu, Junhua; hide

    2018-01-01

    We provide an introduction to a new high-resolution (0.25 degree) global composition forecast produced by NASA's Global Modeling and Assimilation office. The NASA Goddard Earth Observing System version 5 (GEOS-5) model has been expanded to provide global near-real-time forecasts of atmospheric composition at a horizontal resolution of 0.25 degrees (25 km). Previously, this combination of detailed chemistry and resolution was only provided by regional models. This system combines the operational GEOS-5 weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 11) to provide detailed chemical analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). The resolution of the forecasts is the highest resolution compared to current, publically-available global composition forecasts. Evaluation and validation of modeled trace gases and aerosols compared to surface and satellite observations will be presented for constituents relative to health air quality standards. Comparisons of modeled trace gases and aerosols against satellite observations show that the model produces realistic concentrations of atmospheric constituents in the free troposphere. Model comparisons against surface observations highlight the model's capability to capture the diurnal variability of air pollutants under a variety of meteorological conditions. The GEOS-5 composition forecasting system offers a new tool for scientists and the public health community, and is being developed jointly with several government and non-profit partners. Potential applications include air quality warnings, flight campaign planning and exposure studies using the archived analysis fields.

  2. Forecasting effects of global warming on biodiversity

    DEFF Research Database (Denmark)

    Botkin, D.B.; Saxe, H.; Araújo, M.B.

    2007-01-01

    The demand for accurate forecasting of the effects of global warming on biodiversity is growing, but current methods for forecasting have limitations. In this article, we compare and discuss the different uses of four forecasting methods: (1) models that consider species individually, (2) niche...... and theoretical ecological results suggest that many species could be at risk from global warming, during the recent ice ages surprisingly few species became extinct. The potential resolution of this conundrum gives insights into the requirements for more accurate and reliable forecasting. Our eight suggestions...

  3. Fuel cycle forecasting - there are forecasts and there are forecasts

    International Nuclear Information System (INIS)

    Puechl, K.H.

    1975-01-01

    The FORECAST-NUCLEAR computer program described recognizes that forecasts are made to answer a variety of questions and, therefore, that no single forecast is universally appropriate. Also, it recognizes that no two individuals will completely agree as to the input data that are appropriate for obtaining an answer to even a single simple question. Accordingly, the program was written from a utilitarian standpoint: it allows working with multiple projections; data inputting is simple to allow game-playing; computation time is short to minimize the cost of 'what if' assessements; and detail is internally carried to allow meaningful analysis. (author)

  4. Forecasting the demand for new telecommunication services

    DEFF Research Database (Denmark)

    Skouby, Knud Erik; Veiro, Bjørn

    1991-01-01

    A forecasting method that is applicable for new services, where little historical data have been recorded, is proposed. The method uses estimators based on economical, demographic and traffic data. Compared to traditional forecasting procedures that are built upon a solid historical record of dat...

  5. Multiphysics superensemble forecast applied to Mediterranean heavy precipitation situations

    Directory of Open Access Journals (Sweden)

    M. Vich

    2010-11-01

    Full Text Available The high-impact precipitation events that regularly affect the western Mediterranean coastal regions are still difficult to predict with the current prediction systems. Bearing this in mind, this paper focuses on the superensemble technique applied to the precipitation field. Encouraged by the skill shown by a previous multiphysics ensemble prediction system applied to western Mediterranean precipitation events, the superensemble is fed with this ensemble.

    The training phase of the superensemble contributes to the actual forecast with weights obtained by comparing the past performance of the ensemble members and the corresponding observed states. The non-hydrostatic MM5 mesoscale model is used to run the multiphysics ensemble. Simulations are performed with a 22.5 km resolution domain (Domain 1 in forecasts.uib.es" target ="_blank"> http://mm5forecasts.uib.es nested in the ECMWF forecast fields. The period between September and December 2001 is used to train the superensemble and a collection of 19~MEDEX cyclones is used to test it.

    The verification procedure involves testing the superensemble performance and comparing it with that of the poor-man and bias-corrected ensemble mean and the multiphysic EPS control member. The results emphasize the need of a well-behaved training phase to obtain good results with the superensemble technique. A strategy to obtain this improved training phase is already outlined.

  6. Improving Software Reliability Forecasting

    NARCIS (Netherlands)

    Burtsy, Bernard; Albeanu, Grigore; Boros, Dragos N.; Popentiu, Florin; Nicola, V.F.

    1996-01-01

    This work investigates some methods for software reliability forecasting. A supermodel is presented as a suited tool for prediction of reliability in software project development. Also, times series forecasting for cumulative interfailure time is proposed and illustrated.

  7. Global Energy Forecasting Competition 2012

    DEFF Research Database (Denmark)

    Hong, Tao; Pinson, Pierre; Fan, Shu

    2014-01-01

    The Global Energy Forecasting Competition (GEFCom2012) attracted hundreds of participants worldwide, who contributed many novel ideas to the energy forecasting field. This paper introduces both tracks of GEFCom2012, hierarchical load forecasting and wind power forecasting, with details...

  8. Evaluation of probabilistic forecasts with the scoringRules package

    Science.gov (United States)

    Jordan, Alexander; Krüger, Fabian; Lerch, Sebastian

    2017-04-01

    Over the last decades probabilistic forecasts in the form of predictive distributions have become popular in many scientific disciplines. With the proliferation of probabilistic models arises the need for decision-theoretically principled tools to evaluate the appropriateness of models and forecasts in a generalized way in order to better understand sources of prediction errors and to improve the models. Proper scoring rules are functions S(F,y) which evaluate the accuracy of a forecast distribution F , given that an outcome y was observed. In coherence with decision-theoretical principles they allow to compare alternative models, a crucial ability given the variety of theories, data sources and statistical specifications that is available in many situations. This contribution presents the software package scoringRules for the statistical programming language R, which provides functions to compute popular scoring rules such as the continuous ranked probability score for a variety of distributions F that come up in applied work. For univariate variables, two main classes are parametric distributions like normal, t, or gamma distributions, and distributions that are not known analytically, but are indirectly described through a sample of simulation draws. For example, ensemble weather forecasts take this form. The scoringRules package aims to be a convenient dictionary-like reference for computing scoring rules. We offer state of the art implementations of several known (but not routinely applied) formulas, and implement closed-form expressions that were previously unavailable. Whenever more than one implementation variant exists, we offer statistically principled default choices. Recent developments include the addition of scoring rules to evaluate multivariate forecast distributions. The use of the scoringRules package is illustrated in an example on post-processing ensemble forecasts of temperature.

  9. Inaccuracy in traffic forecasts

    DEFF Research Database (Denmark)

    Flyvbjerg, Bent; Holm, Mette K. Skamris; Buhl, Søren Ladegaard

    2006-01-01

    , the difference between actual and forecasted traffic is more than +-20%; for 25% of road projects, the difference is larger than +-40%. Forecasts for roads are more accurate and more balanced than for rail, with no significant difference between the frequency of inflated versus deflated forecasts. But for both...

  10. Managing Sales Forecasters

    NARCIS (Netherlands)

    L.P. de Bruijn (Bert); Ph.H.B.F. Franses (Philip Hans)

    2012-01-01

    textabstractA Forecast Support System (FSS), which generates sales forecasts, is a sophisticated business analytical tool that can help to improve targeted business decisions. Many companies use such a tool, although at the same time they may allow managers to quote their own forecasts. These sales

  11. Predictor-weighting strategies for probabilistic wind power forecasting with an analog ensemble

    Directory of Open Access Journals (Sweden)

    Constantin Junk

    2015-04-01

    Full Text Available Unlike deterministic forecasts, probabilistic predictions provide estimates of uncertainty, which is an additional value for decision-making. Previous studies have proposed the analog ensemble (AnEn, which is a technique to generate uncertainty information from a purely deterministic forecast. The objective of this study is to improve the AnEn performance for wind power forecasts by developing static and dynamic weighting strategies, which optimize the predictor combination with a brute-force continuous ranked probability score (CRPS minimization and a principal component analysis (PCA of the predictors. Predictors are taken from the high-resolution deterministic forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF, including forecasts of wind at several heights, geopotential height, pressure, and temperature, among others. The weighting strategies are compared at five wind farms in Europe and the U.S. situated in regions with different terrain complexity, both on and offshore, and significantly improve the deterministic and probabilistic AnEn forecast performance compared to the AnEn with 10‑m wind speed and direction as predictors and compared to PCA-based approaches. The AnEn methodology also provides reliable estimation of the forecast uncertainty. The optimized predictor combinations are strongly dependent on terrain complexity, local wind regimes, and atmospheric stratification. Since the proposed predictor-weighting strategies can accomplish both the selection of relevant predictors as well as finding their optimal weights, the AnEn performance is improved by up to 20 % at on and offshore sites.

  12. 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)

  13. 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)

  14. Laparoscopy After Previous Laparotomy

    Directory of Open Access Journals (Sweden)

    Zulfo Godinjak

    2006-11-01

    Full Text Available Following the abdominal surgery, extensive adhesions often occur and they can cause difficulties during laparoscopic operations. However, previous laparotomy is not considered to be a contraindication for laparoscopy. The aim of this study is to present that an insertion of Veres needle in the region of umbilicus is a safe method for creating a pneumoperitoneum for laparoscopic operations after previous laparotomy. In the last three years, we have performed 144 laparoscopic operations in patients that previously underwent one or two laparotomies. Pathology of digestive system, genital organs, Cesarean Section or abdominal war injuries were the most common causes of previouslaparotomy. During those operations or during entering into abdominal cavity we have not experienced any complications, while in 7 patients we performed conversion to laparotomy following the diagnostic laparoscopy. In all patients an insertion of Veres needle and trocar insertion in the umbilical region was performed, namely a technique of closed laparoscopy. Not even in one patient adhesions in the region of umbilicus were found, and no abdominal organs were injured.

  15. Information Content of Seasonal Forecasts in a Changing Climate

    Directory of Open Access Journals (Sweden)

    Nir Y. Krakauer

    2013-01-01

    Full Text Available We study the potential value to stakeholders of probabilistic long-term forecasts, as quantified by the mean information gain of the forecast compared to climatology. We use as a case study the USA Climate Prediction Center (CPC forecasts of 3-month temperature and precipitation anomalies made at 0.5-month lead time since 1995. Mean information gain was positive but low (about 2% and 0.5% of the maximum possible for temperature and precipitation forecasts, resp. and has not increased over time. Information-based skill scores showed similar patterns to other, non-information-based, skill scores commonly used for evaluating seasonal forecasts but tended to be smaller, suggesting that information gain is a particularly stringent measure of forecast quality. We also present a new decomposition of forecast information gain into Confidence, Forecast Miscalibration, and Climatology Miscalibration components. Based on this decomposition, the CPC forecasts for temperature are on average underconfident while the precipitation forecasts are overconfident. We apply a probabilistic trend extrapolation method to provide an improved reference seasonal forecast, compared to the current CPC procedure which uses climatology from a recent 30-year period. We show that combining the CPC forecast with the probabilistic trend extrapolation more than doubles the mean information gain, providing one simple avenue for increasing forecast skill.

  16. Forecasting Cryptocurrencies Financial Time Series

    DEFF Research Database (Denmark)

    Catania, Leopoldo; Grassi, Stefano; Ravazzolo, Francesco

    2018-01-01

    This paper studies the predictability of cryptocurrencies time series. We compare several alternative univariate and multivariate models in point and density forecasting of four of the most capitalized series: Bitcoin, Litecoin, Ripple and Ethereum. We apply a set of crypto–predictors and rely...

  17. Macroeconomic models, forecasting, and policymaking

    OpenAIRE

    Pescatori, Andrea; Zaman, Saeed

    2011-01-01

    Models of the macroeconomy have gotten quite sophisticated, thanks to decades of development and advances in computing power. Such models have also become indispensable tools for monetary policymakers, useful both for forecasting and comparing different policy options. Their failure to predict the recent financial crisis does not negate their use, it only points to some areas that can be improved.

  18. Improving the Arctic ice edge forecasts by assimilating high resolution sea ice concentration products in the U.S. Navy's ice forecasting systems

    Science.gov (United States)

    Posey, P. G.; Metzger, E. J.; Wallcraft, A. J.; Hebert, D. A.; Allard, R.; Smedstad, O. M.; Phelps, M.; Fetterer, F. M.; Stewart, S.; Meier, W.; Helfrich, S.

    2016-02-01

    This study presents the improvement in ice edge error within the U.S. Navy's operational sea ice forecast systems gained by assimilating high horizontal resolution satellite-derived ice concentration products. Since the late 1980's, U.S. Navy ice forecast systems have assimilated near real-time sea ice concentration derived from the Defense Meteorological Satellite Program Special Sensor Microwave Imager (SSMI and then SSMIS). The resolution of the SSMI derived product was approximately the same as the previous operational ice forecast system (25 km). As the sea ice forecast model resolution increased over time, the need for higher horizontal resolution observational data grew. In 2013, the Navy's Arctic Cap Nowcast/Forecast System (ACNFS) went into operations with a horizontal resolution of approximately 3.5 km at the North Pole. A method of blending ice concentration observations from the Advanced Microwave Scanning Radiometer 2 (AMSR2) with a sea ice mask produced by the National Ice Center has been developed, resulting in a 4 km ice concentration product. In this study, ACNFS was initialized with this newly developed high resolution blended ice concentration product, and the daily ice edge locations from model hindcast simulations were compared against independent observed ice edge locations. A second evaluation assimilating the new blended sea ice concentration product into the pre-operational Navy Global Ocean Forecast System (GOFS 3.1). This study describes the technique used to create the blended sea ice concentration product and the significant improvements in ice edge forecasting in both of the Navy's sea ice forecasting systems.

  19. Inaccuracy in traffic forecasts

    DEFF Research Database (Denmark)

    Flyvbjerg, Bent; Holm, Mette K. Skamris; Buhl, Søren Ladegaard

    2006-01-01

    that forecasters generally do a poor job of estimating the demand for transportation infrastructure projects. The result is substantial downside financial and economic risk. Forecasts have not become more accurate over the 30-year period studied. If techniques and skills for arriving at accurate demand forecasts...... forecasting. Highly inaccurate traffic forecasts combined with large standard deviations translate into large financial and economic risks. But such risks are typically ignored or downplayed by planners and decision-makers, to the detriment of social and economic welfare. The paper presents the data...

  20. Electricity demand forecasting techniques

    International Nuclear Information System (INIS)

    Gnanalingam, K.

    1994-01-01

    Electricity demand forecasting plays an important role in power generation. The two areas of data that have to be forecasted in a power system are peak demand which determines the capacity (MW) of the plant required and annual energy demand (GWH). Methods used in electricity demand forecasting include time trend analysis and econometric methods. In forecasting, identification of manpower demand, identification of key planning factors, decision on planning horizon, differentiation between prediction and projection (i.e. development of different scenarios) and choosing from different forecasting techniques are important

  1. Oceanic sources of predictability for MJO propagation across the Maritime Continent in a subset of S2S forecast models

    Science.gov (United States)

    DeMott, C. A.; Klingaman, N. P.

    2017-12-01

    Skillful prediction of the Madden-Julian oscillation (MJO) passage across the Maritime Continent (MC) has important implications for global forecasts of high-impact weather events, such as atmospheric rivers and heat waves. The North American teleconnection response to the MJO is strongest when MJO convection is located in the western Pacific Ocean, but many climate and forecast models are deficient in their simulation of MC-crossing MJO events. Compared to atmosphere-only general circulation models (AGCMs), MJO simulation skill generally improves with the addition of ocean feedbacks in coupled GCMs (CGCMs). Using observations, previous studies have noted that the degree of ocean coupling may vary considerably from one MJO event to the next. The coupling mechanisms may be linked to the presence of ocean Equatorial Rossby waves, the sign and amplitude of Equatorial surface currents, and the upper ocean temperature and salinity profiles. In this study, we assess the role of ocean feedbacks to MJO prediction skill using a subset of CGCMs participating in the Subseasonal-to-Seasonal (S2S) Project database. Oceanic observational and reanalysis datasets are used to characterize the upper ocean background state for observed MJO events that do and do not propagate beyond the MC. The ability of forecast models to capture the oceanic influence on the MJO is first assessed by quantifying SST forecast skill. Next, a set of previously developed air-sea interaction diagnostics is applied to model output to measure the role of SST perturbations on the forecast MJO. The "SST effect" in forecast MJO events is compared to that obtained from reanalysis data. Leveraging all ensemble members of a given forecast helps disentangle oceanic model biases from atmospheric model biases, both of which can influence the expression of ocean feedbacks in coupled forecast systems. Results of this study will help identify areas of needed model improvement for improved MJO forecasts.

  2. Forecasting with nonlinear time series model: A Monte-Carlo ...

    African Journals Online (AJOL)

    In this paper, we propose a new method of forecasting with nonlinear time series model using Monte-Carlo Bootstrap method. This new method gives better result in terms of forecast root mean squared error (RMSE) when compared with the traditional Bootstrap method and Monte-Carlo method of forecasting using a ...

  3. Forecasting In One-Dimensional And Generalized Integrated ...

    African Journals Online (AJOL)

    In this paper, forecast of one-dimensional integrated autoregressive bilinear is compared with forecast of generalized integrated autoregressive bilinear model. We describe the method for estimation of these models and the forecast. It is also pointed out that for this class of non-linear time series models; it is possible to ...

  4. Validation of Model Forecasts of the Ambient Solar Wind

    Science.gov (United States)

    Macneice, P. J.; Hesse, M.; Kuznetsova, M. M.; Rastaetter, L.; Taktakishvili, A.

    2009-01-01

    Independent and automated validation is a vital step in the progression of models from the research community into operational forecasting use. In this paper we describe a program in development at the CCMC to provide just such a comprehensive validation for models of the ambient solar wind in the inner heliosphere. We have built upon previous efforts published in the community, sharpened their definitions, and completed a baseline study. We also provide first results from this program of the comparative performance of the MHD models available at the CCMC against that of the Wang-Sheeley-Arge (WSA) model. An important goal of this effort is to provide a consistent validation to all available models. Clearly exposing the relative strengths and weaknesses of the different models will enable forecasters to craft more reliable ensemble forecasting strategies. Models of the ambient solar wind are developing rapidly as a result of improvements in data supply, numerical techniques, and computing resources. It is anticipated that in the next five to ten years, the MHD based models will supplant semi-empirical potential based models such as the WSA model, as the best available forecast models. We anticipate that this validation effort will track this evolution and so assist policy makers in gauging the value of past and future investment in modeling support.

  5. Treatment Rationale and Design for J-AXEL: A Randomized Phase 3 Study Comparing Nab-Paclitaxel With Docetaxel in Patients With Previously Treated Advanced Non-Small-Cell Lung Cancer.

    Science.gov (United States)

    Yoneshima, Yasuto; Morita, Satoshi; Ando, Masahiko; Miura, Satoru; Yoshioka, Hiroshige; Abe, Tetsuya; Kato, Terufumi; Kondo, Masashi; Hosomi, Yukio; Hotta, Katsuyuki; Yamamoto, Nobuyuki; Kishimoto, Junji; Nakanishi, Yoichi; Okamoto, Isamu

    2017-01-01

    Nanoparticle albumin-bound (nab) paclitaxel is a promising new therapeutic agent for all histologic types of non-small-cell lung cancer (NSCLC). We recently performed a phase 2 study of weekly nab-paclitaxel in patients with previously treated advanced NSCLC, finding promising activity and acceptable toxicity for this regimen. We have now designed a randomized phase 3 intergroup study (J-AXEL, UMIN000017487) to examine the clinical benefit and safety of nab-paclitaxel compared to docetaxel in patients with previously treated advanced NSCLC. Patients are randomized to receive either docetaxel (60 mg/m 2 on day 1 every 3 weeks, control arm) or nab-paclitaxel (100 mg/m 2 on days 1, 8, and 15 every 3 weeks, experimental arm), with each drug being administered until disease progression or unacceptable toxicity. The study will evaluate the noninferiority of nab-paclitaxel relative to docetaxel for the primary end point of overall survival. If the primary objective is achieved, this study will provide evidence for a new alternative treatment option for patients with previously treated advanced NSCLC. Copyright © 2016 Elsevier Inc. All rights reserved.

  6. Deepwater Gulf of Mexico more profitable than previously thought

    International Nuclear Information System (INIS)

    Craig, M.J.K.; Hyde, S.T.

    1997-01-01

    Economic evaluations and recent experience show that the deepwater Gulf of Mexico (GOM) is much more profitable than previously thought. Four factors contributing to the changed viewpoint are: First, deepwater reservoirs have proved to have excellent productive capacity, distribution, and continuity when compared to correlative-age shelf deltaic sands. Second, improved technologies and lower perceived risks have lowered the cost of floating production systems (FPSs). Third, projects now get on-line quicker. Fourth, a collection of other important factors are: Reduced geologic risk and associated high success rates for deepwater GOM wells due primarily to improved seismic imaging and processing tools (3D, AVO, etc.); absence of any political risk in the deepwater GOM (common overseas, and very significant in some international areas); and positive impact of deepwater federal royalty relief. This article uses hypothetical reserve distributions and price forecasts to illustrate indicative economics of deepwater prospects. Economics of Shell Oil Co.'s three deepwater projects are also discussed

  7. Patient preference with respect to QoL and reduction in opioid-induced constipation (OIC) after treatment with prolonged-release (PR) oxycodone/naloxone compared with previous analgesic therapy [PREFER study].

    Science.gov (United States)

    van Dongen, V C P C; Vanelderen, P J L; Koopmans-Klein, G; van Megen, Y J B; Van Zundert, J; Huygen, F J P M

    2014-11-01

    The aim of this study was to assess patient preference in terms of quality of life (QoL), analgesia and bowel function for patients with moderate to severe chronic non-malignant pain, when treated with oxycodone PR/naloxone PR compared with the previous WHO-step I and/or WHO-step II analgesic treatment . This was a 3-week open-label phase 3b study conducted in Belgium and the Netherlands, after 3 weeks patients could enter an extension phase. Patient preference with respect to QoL for oxycodone PR/naloxone PR treatment compared with previous WHO-step I and/or WHO-step II analgesics was assessed. A patient was considered a responder with respect to QoL if this assessment was 'better' or 'much better' compared with previous WHO-step I or II analgesics at any time point. Response rate with respect to QoL was 59.2% (95% CI: 51.7-66.8%) for the Full Analysis (FA)-population, for the Per Protocol-population response rate was 71.7% (95% CI: 63.1-80.3%). Explorative analysis showed that response rate with respect to QoL was highest in constipated patients pretreated with WHO-step II analgesics (73.8%). Mean ± SD pain score in the FA-population at start was 74.7 ± 16.6 decreasing to 53.9 ± 24.3 after a median (range) treatment period of 173.5 (31-771) days. For constipated subjects the significant reduction in constipation [improvement of the Bowel Function Index (BFI)], was -24.8 points (95% CI: -17.1 to -32.5). BFI for non-constipated subjects remained well below 28.8. Adverse events with oxycodone PR/naloxone PR treatment were well-known opioid-related adverse events. This study shows that the studied patients previously treated with WHO-step I and/or WHO-step II analgesics prefer treatment with oxycodone PR/naloxone PR with respect to QoL. Moreover, the study shows that treatment with oxycodone PR/naloxone PR significantly reduces OIC in constipated patients and that non-constipated patients do not develop OIC during treatment with oxycodone PR/naloxone PR.

  8. Radiation belt electron flux forecasts: Driving VERB using NARMAX GSO flux forecasts

    Science.gov (United States)

    Walker, S. N.; Balikhin, M. A.; Boynton, R.; Drozdov, A.; Pakhotin, I.; Shprits, Y. Y.

    2016-12-01

    Physics based models, such as VERB, are capable of achieving excellent past-cast and now-cast models of the dynamics of electron fluxes throughout the radiation belt region. Their ability to forecast, however, is strongly dependant upon the accurate forecast of their driving parameters. In contrast, data based models, generated using Systems Science methodologies such as NARMAX, have been shown to achieve highly accurate forecasts over limited spatial domains such as GSO. This paper outlines the use of NARMAX forecasts to drive VERB. Example past-casts are discussed and compared to observations from the Van Allen Probe MagEIS instrument.

  9. Demand forecast model based on CRM

    Science.gov (United States)

    Cai, Yuancui; Chen, Lichao

    2006-11-01

    With interiorizing day by day management thought that regarding customer as the centre, forecasting customer demand becomes more and more important. In the demand forecast of customer relationship management, the traditional forecast methods have very great limitation because much uncertainty of the demand, these all require new modeling to meet the demands of development. In this paper, the notion is that forecasting the demand according to characteristics of the potential customer, then modeling by it. The model first depicts customer adopting uniform multiple indexes. Secondly, the model acquires characteristic customers on the basis of data warehouse and the technology of data mining. The last, there get the most similar characteristic customer by their comparing and forecast the demands of new customer by the most similar characteristic customer.

  10. 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...... a similar opposite effect. A correction for economic growth reduces forecast errors during the second oil crisis but not elsewhere. Industry has a relatively big positive error while transportation has a negative forecast error. Even when the forecast error is small, the results are not so "nice" because...... 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...

  11. Persistent High IgG Phase I Antibody Levels against Coxiella burnetii among Veterinarians Compared to Patients Previously Diagnosed with Acute Q Fever after Three Years of Follow-Up

    Science.gov (United States)

    Wielders, Cornelia C. H.; Boerman, Anneroos W.; Schimmer, Barbara; van den Brom, René; Notermans, Daan W.; van der Hoek, Wim; Schneeberger, Peter M.

    2015-01-01

    Background Little is known about the development of chronic Q fever in occupational risk groups. The aim of this study was to perform long-term follow-up of Coxiella burnetii seropositive veterinarians and investigate the course of IgG phase I and phase II antibodies against C. burnetii antigens and to compare this course with that in patients previously diagnosed with acute Q fever. Methods Veterinarians with IgG phase I ≥1:256 (immunofluorescence assay) that participated in a previous seroprevalence study were asked to provide a second blood sample three years later. IgG antibody profiles were compared to a group of acute Q fever patients who had IgG phase I ≥1:256 twelve months after diagnosis. Results IgG phase I was detected in all veterinarians (n = 76) and in 85% of Q fever patients (n = 98) after three years (pveterinarians and 12% of patients (OR 3.95, 95% CI: 1.84–8.49). Conclusions IgG phase I persists among veterinarians presumably because of continuous exposure to C. burnetii during their work. Serological and clinical follow-up of occupationally exposed risk groups should be considered. PMID:25602602

  12. Verification of operational weather forecasts from the POSEIDON system across the Eastern Mediterranean

    Directory of Open Access Journals (Sweden)

    A. Papadopoulos

    2009-07-01

    Full Text Available The POSEIDON weather forecasting system became operational at the Hellenic Centre for Marine Research (HCMR in October 1999. The system with its nesting capability provided 72-h forecasts in two different model domains, i.e. 25- and 10-km grid spacing. The lower-resolution domain covered an extended area that included most of Europe, Mediterranean Sea and N. Africa, while the higher resolution domain focused on the Eastern Mediterranean. A major upgrade of the system was recently implemented in the framework of the POSEIDON-II project (2005–2008. The aim was to enhance the forecasting skill of the system through improved model parameterization schemes and advanced numerical techniques for assimilating available observations to produce high resolution analysis fields. The configuration of the new system is applied on a horizontal resolution of 1/20°×1/20° (~5 km covering the Mediterranean basin, Black Sea and part of North Atlantic providing up to 5-day forecasts. This paper reviews and compares the current with the previous weather forecasting systems at HCMR presenting quantitative verification statistics from the pre-operational period (from mid-November 2007 to October 2008. The statistics are based on verification against surface observations from the World Meteorological Organization (WMO network across the Eastern Mediterranean region. The results indicate that the use of the new system can significantly improve the weather forecasts.

  13. Analysis of PG&E`s residential end-use metered data to improve electricity demand forecasts -- final report

    Energy Technology Data Exchange (ETDEWEB)

    Eto, J.H.; Moezzi, M.M.

    1993-12-01

    This report summarizes findings from a unique project to improve the end-use electricity load shape and peak demand forecasts made by the Pacific Gas and Electric Company (PG&E) and the California Energy Commission (CEC). First, the direct incorporation of end-use metered data into electricity demand forecasting models is a new approach that has only been made possible by recent end-use metering projects. Second, and perhaps more importantly, the joint-sponsorship of this analysis has led to the development of consistent sets of forecasting model inputs. That is, the ability to use a common data base and similar data treatment conventions for some of the forecasting inputs frees forecasters to concentrate on those differences (between their competing forecasts) that stem from real differences of opinion, rather than differences that can be readily resolved with better data. The focus of the analysis is residential space cooling, which represents a large and growing demand in the PG&E service territory. Using five years of end-use metered, central air conditioner data collected by PG&E from over 300 residences, we developed consistent sets of new inputs for both PG&E`s and CEC`s end-use load shape forecasting models. We compared the performance of the new inputs both to the inputs previously used by PG&E and CEC, and to a second set of new inputs developed to take advantage of a recently added modeling option to the forecasting model. The testing criteria included ability to forecast total daily energy use, daily peak demand, and demand at 4 P.M. (the most frequent hour of PG&E`s system peak demand). We also tested the new inputs with the weather data used by PG&E and CEC in preparing their forecasts.

  14. Ensemble forecasts of road surface temperatures

    Science.gov (United States)

    Sokol, Zbyněk; Bližňák, Vojtěch; Sedlák, Pavel; Zacharov, Petr; Pešice, Petr; Škuthan, Miroslav

    2017-05-01

    This paper describes a new ensemble technique for road surface temperature (RST) forecasting using an energy balance and heat conduction model. Compared to currently used deterministic forecasts, the proposed technique allows the estimation of forecast uncertainty and probabilistic forecasts. The ensemble technique is applied to the METRo-CZ model and stems from error covariance analyses of the forecasted air temperature and humidity 2 m above the ground, wind speed at 10 m and total cloud cover N in octas by the numerical weather prediction (NWP) model. N is used to estimate the shortwave and longwave radiation fluxes. These variables are used to calculate the boundary conditions in the METRo-CZ model. We found that the variable N is crucial for generating the ensembles. Nevertheless, the ensemble spread is too small and underestimates the uncertainty in the RST forecast. One of the reasons is not considering errors in the rain and snow forecast by the NWP model when generating ensembles. Technical issues, such as incorrect sky view factors and the current state of road surface conditions also contribute to errors. Although the ensemble technique underestimates the uncertainty in the RST forecasts, it provides additional information to road authorities who provide winter road maintenance.

  15. Forecasting phenology under global warming.

    Science.gov (United States)

    Ibáñez, Inés; Primack, Richard B; Miller-Rushing, Abraham J; Ellwood, Elizabeth; Higuchi, Hiroyoshi; Lee, Sang Don; Kobori, Hiromi; Silander, John A

    2010-10-12

    As a consequence of warming temperatures around the world, spring and autumn phenologies have been shifting, with corresponding changes in the length of the growing season. Our understanding of the spatial and interspecific variation of these changes, however, is limited. Not all species are responding similarly, and there is significant spatial variation in responses even within species. This spatial and interspecific variation complicates efforts to predict phenological responses to ongoing climate change, but must be incorporated in order to build reliable forecasts. Here, we use a long-term dataset (1953-2005) of plant phenological events in spring (flowering and leaf out) and autumn (leaf colouring and leaf fall) throughout Japan and South Korea to build forecasts that account for these sources of variability. Specifically, we used hierarchical models to incorporate the spatial variability in phenological responses to temperature to then forecast species' overall and site-specific responses to global warming. We found that for most species, spring phenology is advancing and autumn phenology is getting later, with the timing of events changing more quickly in autumn compared with the spring. Temporal trends and phenological responses to temperature in East Asia contrasted with results from comparable studies in Europe, where spring events are changing more rapidly than are autumn events. Our results emphasize the need to study multiple species at many sites to understand and forecast regional changes in phenology.

  16. Forecasting phenology under global warming

    Science.gov (United States)

    Ibáñez, Inés; Primack, Richard B.; Miller-Rushing, Abraham J.; Ellwood, Elizabeth; Higuchi, Hiroyoshi; Lee, Sang Don; Kobori, Hiromi; Silander, John A.

    2010-01-01

    As a consequence of warming temperatures around the world, spring and autumn phenologies have been shifting, with corresponding changes in the length of the growing season. Our understanding of the spatial and interspecific variation of these changes, however, is limited. Not all species are responding similarly, and there is significant spatial variation in responses even within species. This spatial and interspecific variation complicates efforts to predict phenological responses to ongoing climate change, but must be incorporated in order to build reliable forecasts. Here, we use a long-term dataset (1953–2005) of plant phenological events in spring (flowering and leaf out) and autumn (leaf colouring and leaf fall) throughout Japan and South Korea to build forecasts that account for these sources of variability. Specifically, we used hierarchical models to incorporate the spatial variability in phenological responses to temperature to then forecast species' overall and site-specific responses to global warming. We found that for most species, spring phenology is advancing and autumn phenology is getting later, with the timing of events changing more quickly in autumn compared with the spring. Temporal trends and phenological responses to temperature in East Asia contrasted with results from comparable studies in Europe, where spring events are changing more rapidly than are autumn events. Our results emphasize the need to study multiple species at many sites to understand and forecast regional changes in phenology. PMID:20819816

  17. 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...

  18. Uncertainty and Disagreement in Forecasting Inflation : Evidence from the Laboratory (Revised version of CentER DP 2011-053)

    NARCIS (Netherlands)

    Pfajfar, D.; Zakelj, B.

    2012-01-01

    Abstract: This paper compares the behavior of subjects' uncertainty in different monetary policy environments when forecasting inflation in the laboratory. We find that inflation targeting produces lower uncertainty and higher accuracy of interval forecasts than inflation forecast targeting. We also

  19. Uncertainty and Disagreement in Forecasting Inflation : Evidence from the Laboratory (Revised version of EBC DP 2011-014)

    NARCIS (Netherlands)

    Pfajfar, D.; Zakelj, B.

    2012-01-01

    Abstract: This paper compares the behavior of subjects' uncertainty in different monetary policy environments when forecasting inflation in the laboratory. We find that inflation targeting produces lower uncertainty and higher accuracy of interval forecasts than inflation forecast targeting. We also

  20. Development of nonlinear empirical models to forecast daily PM2.5 and ozone levels in three large Chinese cities

    Science.gov (United States)

    Lv, Baolei; Cobourn, W. Geoffrey; Bai, Yuqi

    2016-12-01

    Empirical regression models for next-day forecasting of PM2.5 and O3 air pollution concentrations have been developed and evaluated for three large Chinese cities, Beijing, Nanjing and Guangzhou. The forecast models are empirical nonlinear regression models designed for use in an automated data retrieval and forecasting platform. The PM2.5 model includes an upwind air quality variable, PM24, to account for regional transport of PM2.5, and a persistence variable (previous day PM2.5 concentration). The models were evaluated in the hindcast mode with a two-year air quality and meteorological data set using a leave-one-month-out cross validation method, and in the forecast mode with a one-year air quality and forecasted weather dataset that included forecasted air trajectories. The PM2.5 models performed well in the hindcast mode, with coefficient of determination (R2) values of 0.54, 0.65 and 0.64, and normalized mean error (NME) values of 0.40, 0.26 and 0.23 respectively, for the three cities. The O3 models also performed well in the hindcast mode, with R2 values of 0.75, 0.55 and 0.73, and NME values of 0.29, 0.26 and 0.24 in the three cities. The O3 models performed better in summertime than in winter in Beijing and Guangzhou, and captured the O3 variations well all the year round in Nanjing. The overall forecast performance of the PM2.5 and O3 models during the test year varied from fair to good, depending on location. The forecasts were somewhat degraded compared with hindcasts from the same year, depending on the accuracy of the forecasted meteorological input data. For the O3 models, the model forecast accuracy was strongly dependent on the maximum temperature forecasts. For the critical forecasts, involving air quality standard exceedences, the PM2.5 model forecasts were fair to good, and the O3 model forecasts were poor to fair.

  1. Forecasting in Planning

    OpenAIRE

    Ike, P.; Voogd, Henk; Voogd, Henk; Linden, Gerard

    2004-01-01

    This chapter begins with a discussion of qualitative forecasting by describing a number of methods that depend on judgements made by stakeholders, experts or other interested parties to arrive at forecasts. Two qualitative approaches are illuminated, the Delphi and scenario methods respectively. Quantitative forecasting is illustrated with a brief overview of time series methods. Both qualitative and quantitative methods are illustrated by an example. The role and relative importance of forec...

  2. Commuter Airline Forecasts,

    Science.gov (United States)

    1981-05-01

    conterminous United States (48 contiguous states and the District of Columbia), for the State of Hawaii, and for the U.S. Carribean areas, Puerto Rico and U.S...FAA 15. Supplementary Notes I Abstract This publication presents forecasts of cammuter air carrier activity and describes the models designed for...forecasting Contenninous United States, Puerto Rico and the Virgin Islands, Hawaii, and individual airport activity. These forecasts take into account the

  3. ECMWF Extreme Forecast Index for water vapor transport: A forecast tool for atmospheric rivers and extreme precipitation

    Science.gov (United States)

    Lavers, David A.; Pappenberger, Florian; Richardson, David S.; Zsoter, Ervin

    2016-11-01

    In winter, heavy precipitation and floods along the west coasts of midlatitude continents are largely caused by intense water vapor transport (integrated vapor transport (IVT)) within the atmospheric river of extratropical cyclones. This study builds on previous findings that showed that forecasts of IVT have higher predictability than precipitation, by applying and evaluating the European Centre for Medium-Range Weather Forecasts Extreme Forecast Index (EFI) for IVT in ensemble forecasts during three winters across Europe. We show that the IVT EFI is more able (than the precipitation EFI) to capture extreme precipitation in forecast week 2 during forecasts initialized in a positive North Atlantic Oscillation (NAO) phase; conversely, the precipitation EFI is better during the negative NAO phase and at shorter leads. An IVT EFI example for storm Desmond in December 2015 highlights its potential to identify upcoming hydrometeorological extremes, which may prove useful to the user and forecasting communities.

  4. A Novel Nonlinear Combined Forecasting System for Short-Term Load Forecasting

    Directory of Open Access Journals (Sweden)

    Chengshi Tian

    2018-03-01

    Full Text Available Short-term load forecasting plays an indispensable role in electric power systems, which is not only an extremely challenging task but also a concerning issue for all society due to complex nonlinearity characteristics. However, most previous combined forecasting models were based on optimizing weight coefficients to develop a linear combined forecasting model, while ignoring that the linear combined model only considers the contribution of the linear terms to improving the model’s performance, which will lead to poor forecasting results because of the significance of the neglected and potential nonlinear terms. In this paper, a novel nonlinear combined forecasting system, which consists of three modules (improved data pre-processing module, forecasting module and the evaluation module is developed for short-term load forecasting. Different from the simple data pre-processing of most previous studies, the improved data pre-processing module based on longitudinal data selection is successfully developed in this system, which further improves the effectiveness of data pre-processing and then enhances the final forecasting performance. Furthermore, the modified support vector machine is developed to integrate all the individual predictors and obtain the final prediction, which successfully overcomes the upper drawbacks of the linear combined model. Moreover, the evaluation module is incorporated to perform a scientific evaluation for the developed system. The half-hourly electrical load data from New South Wales are employed to verify the effectiveness of the developed forecasting system, and the results reveal that the developed nonlinear forecasting system can be employed in the dispatching and planning for smart grids.

  5. Adaptive correction of ensemble forecasts

    Science.gov (United States)

    Pelosi, Anna; Battista Chirico, Giovanni; Van den Bergh, Joris; Vannitsem, Stephane

    2017-04-01

    Forecasts from numerical weather prediction (NWP) models often suffer from both systematic and non-systematic errors. These are present in both deterministic and ensemble forecasts, and originate from various sources such as model error and subgrid variability. Statistical post-processing techniques can partly remove such errors, which is particularly important when NWP outputs concerning surface weather variables are employed for site specific applications. Many different post-processing techniques have been developed. For deterministic forecasts, adaptive methods such as the Kalman filter are often used, which sequentially post-process the forecasts by continuously updating the correction parameters as new ground observations become available. These methods are especially valuable when long training data sets do not exist. For ensemble forecasts, well-known techniques are ensemble model output statistics (EMOS), and so-called "member-by-member" approaches (MBM). Here, we introduce a new adaptive post-processing technique for ensemble predictions. The proposed method is a sequential Kalman filtering technique that fully exploits the information content of the ensemble. One correction equation is retrieved and applied to all members, however the parameters of the regression equations are retrieved by exploiting the second order statistics of the forecast ensemble. We compare our new method with two other techniques: a simple method that makes use of a running bias correction of the ensemble mean, and an MBM post-processing approach that rescales the ensemble mean and spread, based on minimization of the Continuous Ranked Probability Score (CRPS). We perform a verification study for the region of Campania in southern Italy. We use two years (2014-2015) of daily meteorological observations of 2-meter temperature and 10-meter wind speed from 18 ground-based automatic weather stations distributed across the region, comparing them with the corresponding COSMO

  6. How is the weather? Forecasting inpatient glycemic control.

    Science.gov (United States)

    Saulnier, George E; Castro, Janna C; Cook, Curtiss B; Thompson, Bithika M

    2017-11-01

    Apply methods of damped trend analysis to forecast inpatient glycemic control. Observed and calculated point-of-care blood glucose data trends were determined over 62 weeks. Mean absolute percent error was used to calculate differences between observed and forecasted values. Comparisons were drawn between model results and linear regression forecasting. The forecasted mean glucose trends observed during the first 24 and 48 weeks of projections compared favorably to the results provided by linear regression forecasting. However, in some scenarios, the damped trend method changed inferences compared with linear regression. In all scenarios, mean absolute percent error values remained below the 10% accepted by demand industries. Results indicate that forecasting methods historically applied within demand industries can project future inpatient glycemic control. Additional study is needed to determine if forecasting is useful in the analyses of other glucometric parameters and, if so, how to apply the techniques to quality improvement.

  7. Combined time-varying forecast based on the proper scoring approach for wind power generation

    DEFF Research Database (Denmark)

    Chen, Xingying; Jiang, Yu; Yu, Kun

    2017-01-01

    Compared with traditional point forecasts, combined forecast have been proposed as an effective method to provide more accurate forecasts than individual model. However, the literature and research focus on wind-power combined forecasts are relatively limited. Here, based on forecasting error...... distribution, a proper scoring approach is applied to combine plausible models to form an overall time-varying model for the next day forecasts, rather than weights-based combination. To validate the effectiveness of the proposed method, real data of 3 years were used for testing. Simulation results...... demonstrate that the proposed method improves the accuracy of overall forecasts, even compared with a numerical weather prediction....

  8. Coherent forecasts of mortality with compositional data analysis

    Directory of Open Access Journals (Sweden)

    Marie-Pier Bergeron-Boucher

    2017-08-01

    Full Text Available Background: Mortality trends for subpopulations, e.g., countries in a region or provinces in a country, tend to change similarly over time. However, when forecasting subpopulations independently, the forecast mortality trends often diverge. These divergent trends emerge from an inability of different forecast models to offer population-specific forecasts that are consistent with one another. Nondivergent forecasts between similar populations are often referred to as "coherent." Methods: We propose a new forecasting method that addresses the coherence problem for subpopulations, based on Compositional Data Analysis (CoDa of the life table distribution of deaths. We adapt existing coherent and noncoherent forecasting models to CoDa and compare their results. Results: We apply our coherent method to the female mortality of 15 Western European countries and show that our proposed strategy would have improved the forecast accuracy for many of the selected countries. The results also show that the CoDa adaptation of commonly used models allows the rates of mortality improvements (RMIs to change over time. Contribution: This study opens a discussion about the use of age-specific mortality indicators other than death rates to forecast mortality. The results show that the use of life table deaths and CoDa leads to less biased forecasts than more commonly used forecasting models based on the extrapolation of death rates. To the authors' knowledge, the present study is the first attempt to forecast coherently the distribution of deaths of many populations.

  9. 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.

  10. World Area Forecast System (WAFS)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The World Area Forecast System (WAFS) is a worldwide system by which world area forecast centers provide aeronautical meteorological en-route forecasts in uniform...

  11. Forecasting in Planning

    NARCIS (Netherlands)

    Ike, P.; Voogd, Henk; Voogd, Henk; Linden, Gerard

    2004-01-01

    This chapter begins with a discussion of qualitative forecasting by describing a number of methods that depend on judgements made by stakeholders, experts or other interested parties to arrive at forecasts. Two qualitative approaches are illuminated, the Delphi and scenario methods respectively.

  12. Real-time bias-adjusted O 3 and PM 2.5 air quality index forecasts and their performance evaluations over the continental United States

    Science.gov (United States)

    Kang, Daiwen; Mathur, Rohit; Trivikrama Rao, S.

    2010-06-01

    The National Air Quality Forecast Capacity (NAQFC) system, which links NOAA's North American Mesoscale (NAM) meteorological model with EPA's Community Multiscale Air Quality (CMAQ) model, provided operational ozone (O 3) and experimental fine particular matter (PM 2.5) forecasts over the continental United States (CONUS) during 2008. This paper describes the implementation of a real-time Kalman Filter (KF) bias-adjustment technique to improve the accuracy of O 3 and PM 2.5 forecasts at discrete monitoring locations. The operational surface-level O 3 and PM 2.5 forecasts from the NAQFC system were post-processed by the KF bias-adjusted technique using near real-time hourly O 3 and PM 2.5 observations obtained from EPA's AIRNow measurement network. The KF bias-adjusted forecasts were created daily, providing 24-h hourly bias-adjusted forecasts for O 3 and PM 2.5 at all AIRNow monitoring sites within the CONUS domain. The bias-adjustment post-processing implemented in this study requires minimal computational cost; requiring less than 10 min of CPU on a single processor Linux machine to generate 24-h hourly bias-adjusted forecasts over the entire CONUS domain. The results show that the real-time KF bias-adjusted forecasts for both O 3 and PM 2.5 have performed as well as or even better than the previous studies when the same technique was applied to the historical O 3 and PM 2.5 time series from archived AQF in earlier years. Compared to the raw forecasts, the KF forecasts displayed significant improvement in the daily maximum 8-h O 3 and daily mean PM 2.5 forecasts in terms of both discrete (i.e., reduced errors, increased correlation coefficients, and index of agreement) and categorical (increased hit rate and decreased false alarm ratio) evaluation metrics at almost all locations during the study period in 2008.

  13. Evaluating probabilistic dengue risk forecasts from a prototype early warning system for Brazil

    Science.gov (United States)

    Lowe, Rachel; Coelho, Caio AS; Barcellos, Christovam; Carvalho, Marilia Sá; Catão, Rafael De Castro; Coelho, Giovanini E; Ramalho, Walter Massa; Bailey, Trevor C; Stephenson, David B; Rodó, Xavier

    2016-01-01

    Recently, a prototype dengue early warning system was developed to produce probabilistic forecasts of dengue risk three months ahead of the 2014 World Cup in Brazil. Here, we evaluate the categorical dengue forecasts across all microregions in Brazil, using dengue cases reported in June 2014 to validate the model. We also compare the forecast model framework to a null model, based on seasonal averages of previously observed dengue incidence. When considering the ability of the two models to predict high dengue risk across Brazil, the forecast model produced more hits and fewer missed events than the null model, with a hit rate of 57% for the forecast model compared to 33% for the null model. This early warning model framework may be useful to public health services, not only ahead of mass gatherings, but also before the peak dengue season each year, to control potentially explosive dengue epidemics. DOI: http://dx.doi.org/10.7554/eLife.11285.001 PMID:26910315

  14. Wheat Yield Forecasting for Punjab Province from Vegetation Index Time Series and Historic Crop Statistics

    Directory of Open Access Journals (Sweden)

    Jan Dempewolf

    2014-10-01

    Full Text Available Policy makers, government planners and agricultural market participants in Pakistan require accurate and timely information about wheat yield and production. Punjab Province is by far the most important wheat producing region in the country. The manual collection of field data and data processing for crop forecasting by the provincial government requires significant amounts of time before official reports can be released. Several studies have shown that wheat yield can be effectively forecast using satellite remote sensing data. In this study, we developed a methodology for estimating wheat yield and area for Punjab Province from freely available Landsat and MODIS satellite imagery approximately six weeks before harvest. Wheat yield was derived by regressing reported yield values against time series of four different peak-season MODIS-derived vegetation indices. We also tested deriving wheat area from the same MODIS time series using a regression-tree approach. Among the four evaluated indices, WDRVI provided more consistent and accurate yield forecasts compared to NDVI, EVI2 and saturation-adjusted normalized difference vegetation index (SANDVI. The lowest RMSE values at the district level for forecast versus reported yield were found when using six or more years of training data. Forecast yield for the 2007/2008 to 2012/2013 growing seasons were within 0.2% and 11.5% of final reported values. Absolute deviations of wheat area and production forecasts from reported values were slightly greater compared to using the previous year's or the three- or six-year moving average values, implying that 250-m MODIS data does not provide sufficient spatial resolution for providing improved wheat area and production forecasts.

  15. Weather forecasting based on hybrid neural model

    Science.gov (United States)

    Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.

    2017-11-01

    Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.

  16. A Two-Dimensional Gridded Solar Forecasting System using Situation-Dependent Blending of Multiple Weather Models

    Science.gov (United States)

    Lu, S.; Hwang, Y.; Shao, X.; Hamann, H.

    2015-12-01

    Previously, we reported the application of a "weather situation" dependent multi-model blending approach to improve the forecast accuracy of solar irradiance and other atmospheric parameters. The approach uses machine-learning techniques to classify "weather situations" by a set of atmospheric parameters. The "weather situation" classification is location-dependent and each "weather situation" has characteristic forecast errors from a set of individual input numerical weather prediction (NWP) models. The input models are thus corrected or combined differently for different "weather situations" to minimize the overall forecast error. While the original implementation of the model-blending is applicable to only point-like locations having historical data of both measurements and forecasts, here we extend the approach to provide two-dimensional (2D) gridded forecasts. An experimental 2D forecasting system has been set up to provide gridded forecasts of solar irradiance (global horizontal irradiance), temperature, wind speed, and humidity for the contiguous United States (CONUS). Validation results show around 30% enhancement of 0 to 48 hour ahead solar irradiance forecast accuracy compared to the best input NWP model. The forecasting system may be leveraged by other site- or region-specific solar energy forecast products. To enable the 2D forecasting system, historical solar irradiance measurements from around 1,600 selected sites of the remote automated weather stations (RAWS) network have been employed. The CONUS was divided into smaller sub-regions, each containing a group of 10 to 20 RAWS sites. A group of sites, as classified by statistical analysis, have similar "weather patterns", i.e. the NWPs have similar "weather situation" dependent forecast errors for all sites in a group. The model-blending trained by the historical data from a group of sites is then applied for all locations in the corresponding sub-region. We discuss some key techniques developed for

  17. ENSO-based probabilistic forecasts of March-May U.S. tornado and hail activity

    Science.gov (United States)

    Lepore, Chiara; Tippett, Michael K.; Allen, John T.

    2017-09-01

    Extended logistic regression is used to predict March-May severe convective storm (SCS) activity based on the preceding December-February (DJF) El Niño-Southern Oscillation (ENSO) state. The spatially resolved probabilistic forecasts are verified against U.S. tornado counts, hail events, and two environmental indices for severe convection. The cross-validated skill is positive for roughly a quarter of the U.S. Overall, indices are predicted with more skill than are storm reports, and hail events are predicted with more skill than tornado counts. Skill is higher in the cool phase of ENSO (La Niña like) when overall SCS activity is higher. SCS forecasts based on the predicted DJF ENSO state from coupled dynamical models initialized in October of the previous year extend the lead time with only a modest reduction in skill compared to forecasts based on the observed DJF ENSO state.

  18. Clinical significance of previously cryptic copy number alterations and loss of heterozygosity in pediatric acute myeloid leukemia and myelodysplastic syndrome determined using combined array comparative genomic hybridization plus single-nucleotide polymorphism microarray analyses.

    Science.gov (United States)

    Koh, Kyung-Nam; Lee, Jin Ok; Seo, Eul Ju; Lee, Seong Wook; Suh, Jin Kyung; Im, Ho Joon; Seo, Jong Jin

    2014-07-01

    The combined array comparative genomic hybridization plus single-nucleotide polymorphism microarray (CGH+SNP microarray) platform can simultaneously detect copy number alterations (CNA) and copy-neutral loss of heterozygosity (LOH). Eighteen children with acute myeloid leukemia (AML) (n=15) or myelodysplastic syndrome (MDS) (n=3) were studied using CGH+SNP microarray to evaluate the clinical significance of submicroscopic chromosomal aberrations. CGH+SNP microarray revealed CNAs at 14 regions in 9 patients, while metaphase cytogenetic (MC) analysis detected CNAs in 11 regions in 8 patients. Using CGH+SNP microarray, LOHs>10 Mb involving terminal regions or the whole chromosome were detected in 3 of 18 patients (17%). CGH+SNP microarray revealed cryptic LOHs with or without CNAs in 3 of 5 patients with normal karyotypes. CGH+SNP microarray detected additional cryptic CNAs (n=2) and LOHs (n=5) in 6 of 13 patients with abnormal MC. In total, 9 patients demonstrated additional aberrations, including CNAs (n=3) and/or LOHs (n=8). Three of 15 patients with AML and terminal LOH>10 Mb demonstrated a significantly inferior relapse-free survival rate (P=0.041). This study demonstrates that CGH+SNP microarray can simultaneously detect previously cryptic CNAs and LOH, which may demonstrate prognostic implications.

  19. East Asian winter monsoon forecasting schemes based on the NCEP's climate forecast system

    Science.gov (United States)

    Tian, Baoqiang; Fan, Ke; Yang, Hongqing

    2017-12-01

    The East Asian winter monsoon (EAWM) is the major climate system in the Northern Hemisphere during boreal winter. In this study, we developed two schemes to improve the forecasting skill of the interannual variability of the EAWM index (EAWMI) using the interannual increment prediction method, also known as the DY method. First, we found that version 2 of the NCEP's Climate Forecast System (CFSv2) showed higher skill in predicting the EAWMI in DY form than not. So, based on the advantage of the DY method, Scheme-I was obtained by adding the EAWMI DY predicted by CFSv2 to the observed EAWMI in the previous year. This scheme showed higher forecasting skill than CFSv2. Specifically, during 1983-2016, the temporal correlation coefficient between the Scheme-I-predicted and observed EAWMI was 0.47, exceeding the 99% significance level, with the root-mean-square error (RMSE) decreased by 12%. The autumn Arctic sea ice and North Pacific sea surface temperature (SST) are two important external forcing factors for the interannual variability of the EAWM. Therefore, a second (hybrid) prediction scheme, Scheme-II, was also developed. This scheme not only involved the EAWMI DY of CFSv2, but also the sea-ice concentration (SIC) observed the previous autumn in the Laptev and East Siberian seas and the temporal coefficients of the third mode of the North Pacific SST in DY form. We found that a negative SIC anomaly in the preceding autumn over the Laptev and the East Siberian seas could lead to a significant enhancement of the Aleutian low and East Asian westerly jet in the following winter. However, the intensity of the winter Siberian high was mainly affected by the third mode of the North Pacific autumn SST. Scheme-I and Scheme-II also showed higher predictive ability for the EAWMI in negative anomaly years compared to CFSv2. More importantly, the improvement in the prediction skill of the EAWMI by the new schemes, especially for Scheme-II, could enhance the forecasting skill of

  20. Short-Term Forecasting Models for Photovoltaic Plants: Analytical versus Soft-Computing Techniques

    OpenAIRE

    Monteiro, Claudio; Fernandez-Jimenez, L. Alfredo; Ramirez-Rosado, Ignacio J.; Muñoz-Jimenez, Andres; Lara-Santillan, Pedro M.

    2013-01-01

    We present and compare two short-term statistical forecasting models for hourly average electric power production forecasts of photovoltaic (PV) plants: the analytical PV power forecasting model (APVF) and the multiplayer perceptron PV forecasting model (MPVF). Both models use forecasts from numerical weather prediction (NWP) tools at the location of the PV plant as well as the past recorded values of PV hourly electric power production. The APVF model consists of an original modeling for adj...

  1. Net load forecasting for high renewable energy penetration grids

    International Nuclear Information System (INIS)

    Kaur, Amanpreet; Nonnenmacher, Lukas; Coimbra, Carlos F.M.

    2016-01-01

    We discuss methods for net load forecasting and their significance for operation and management of power grids with high renewable energy penetration. Net load forecasting is an enabling technology for the integration of microgrid fleets with the macrogrid. Net load represents the load that is traded between the grids (microgrid and utility grid). It is important for resource allocation and electricity market participation at the point of common coupling between the interconnected grids. We compare two inherently different approaches: additive and integrated net load forecast models. The proposed methodologies are validated on a microgrid with 33% annual renewable energy (solar) penetration. A heuristics based solar forecasting technique is proposed, achieving skill of 24.20%. The integrated solar and load forecasting model outperforms the additive model by 10.69% and the uncertainty range for the additive model is larger than the integrated model by 2.2%. Thus, for grid applications an integrated forecast model is recommended. We find that the net load forecast errors and the solar forecasting errors are cointegrated with a common stochastic drift. This is useful for future planning and modeling because the solar energy time-series allows to infer important features of the net load time-series, such as expected variability and uncertainty. - Highlights: • Net load forecasting methods for grids with renewable energy generation are discussed. • Integrated solar and load forecasting outperforms the additive model by 10.69%. • Net load forecasting reduces the uncertainty between the interconnected grids.

  2. Utilizing Climate Forecasts for Improving Water and Power Systems Coordination

    Science.gov (United States)

    Arumugam, S.; Queiroz, A.; Patskoski, J.; Mahinthakumar, K.; DeCarolis, J.

    2016-12-01

    Climate forecasts, typically monthly-to-seasonal precipitation forecasts, are commonly used to develop streamflow forecasts for improving reservoir management. Irrespective of their high skill in forecasting, temperature forecasts in developing power demand forecasts are not often considered along with streamflow forecasts for improving water and power systems coordination. In this study, we consider a prototype system to analyze the utility of climate forecasts, both precipitation and temperature, for improving water and power systems coordination. The prototype system, a unit-commitment model that schedules power generation from various sources, is considered and its performance is compared with an energy system model having an equivalent reservoir representation. Different skill sets of streamflow forecasts and power demand forecasts are forced on both water and power systems representations for understanding the level of model complexity required for utilizing monthly-to-seasonal climate forecasts to improve coordination between these two systems. The analyses also identify various decision-making strategies - forward purchasing of fuel stocks, scheduled maintenance of various power systems and tradeoff on water appropriation between hydropower and other uses - in the context of various water and power systems configurations. Potential application of such analyses for integrating large power systems with multiple river basins is also discussed.

  3. NASA Products to Enhance Energy Utility Load Forecasting

    Science.gov (United States)

    Lough, G.; Zell, E.; Engel-Cox, J.; Fungard, Y.; Jedlovec, G.; Stackhouse, P.; Homer, R.; Biley, S.

    2012-01-01

    Existing energy load forecasting tools rely upon historical load and forecasted weather to predict load within energy company service areas. The shortcomings of load forecasts are often the result of weather forecasts that are not at a fine enough spatial or temporal resolution to capture local-scale weather events. This project aims to improve the performance of load forecasting tools through the integration of high-resolution, weather-related NASA Earth Science Data, such as temperature, relative humidity, and wind speed. Three companies are participating in operational testing one natural gas company, and two electric providers. Operational results comparing load forecasts with and without NASA weather forecasts have been generated since March 2010. We have worked with end users at the three companies to refine selection of weather forecast information and optimize load forecast model performance. The project will conclude in 2012 with transitioning documented improvements from the inclusion of NASA forecasts for sustained use by energy utilities nationwide in a variety of load forecasting tools. In addition, Battelle has consulted with energy companies nationwide to document their information needs for long-term planning, in light of climate change and regulatory impacts.

  4. Action-based flood forecasting for triggering humanitarian action

    Science.gov (United States)

    Coughlan de Perez, Erin; van den Hurk, Bart; van Aalst, Maarten K.; Amuron, Irene; Bamanya, Deus; Hauser, Tristan; Jongma, Brenden; Lopez, Ana; Mason, Simon; Mendler de Suarez, Janot; Pappenberger, Florian; Rueth, Alexandra; Stephens, Elisabeth; Suarez, Pablo; Wagemaker, Jurjen; Zsoter, Ervin

    2016-09-01

    Too often, credible scientific early warning information of increased disaster risk does not result in humanitarian action. With financial resources tilted heavily towards response after a disaster, disaster managers have limited incentive and ability to process complex scientific data, including uncertainties. These incentives are beginning to change, with the advent of several new forecast-based financing systems that provide funding based on a forecast of an extreme event. Given the changing landscape, here we demonstrate a method to select and use appropriate forecasts for specific humanitarian disaster prevention actions, even in a data-scarce location. This action-based forecasting methodology takes into account the parameters of each action, such as action lifetime, when verifying a forecast. Forecasts are linked with action based on an understanding of (1) the magnitude of previous flooding events and (2) the willingness to act "in vain" for specific actions. This is applied in the context of the Uganda Red Cross Society forecast-based financing pilot project, with forecasts from the Global Flood Awareness System (GloFAS). Using this method, we define the "danger level" of flooding, and we select the probabilistic forecast triggers that are appropriate for specific actions. Results from this methodology can be applied globally across hazards and fed into a financing system that ensures that automatic, pre-funded early action will be triggered by forecasts.

  5. Spatial electric load forecasting

    CERN Document Server

    Willis, H Lee

    2002-01-01

    Spatial Electric Load Forecasting Consumer Demand for Power and ReliabilityCoincidence and Load BehaviorLoad Curve and End-Use ModelingWeather and Electric LoadWeather Design Criteria and Forecast NormalizationSpatial Load Growth BehaviorSpatial Forecast Accuracy and Error MeasuresTrending MethodsSimulation Method: Basic ConceptsA Detailed Look at the Simulation MethodBasics of Computerized SimulationAnalytical Building Blocks for Spatial SimulationAdvanced Elements of Computerized SimulationHybrid Trending-Simulation MethodsAdvanced

  6. Tsunami Forecast for Galapagos Islands

    Science.gov (United States)

    Renteria, W.

    2012-04-01

    The objective of this study is to present a model for the short-term and long-term tsunami forecast for Galapagos Islands. For both cases the ComMIT/MOST(Titov,et al 2011) numerical model and methodology have been used. The results for the short-term model has been compared with the data from Lynett et al, 2011 surveyed from the impacts of the March/11 in the Galapagos Islands. For the case of long-term forecast, several scenarios have run along the Pacific, an extreme flooding map is obtained, the method is considered suitable for places with poor or without tsunami impact information, but under tsunami risk geographic location.

  7. Groundwater Forecasting Optimization Pertain to Dam Removal

    Science.gov (United States)

    Brown, L.; Berthelote, A. R.

    2011-12-01

    There is increasing interest in removing dams due to changing ecological and societal values. Groundwater recharge rate is closely connected to reservoir presence or absence. With the removal of dams and their associated reservoirs, reductions in groundwater levels are likely to impact water supplies for domestic, industrial and agricultural use. Therefore accessible economic and time effective tools to forecast groundwater level declines with acceptable uncertainty following dam removals are critical for public welfare and healthy regional economies. These tools are also vital to project planning and provide beneficial information for restoration and remediation managements. The standard tool for groundwater forecasting is 3D Numerical modeling. Artificial Neural Networks (ANNs) may be an alternative tool for groundwater forecasting pertain to dam removal. This project compared these two tools throughout the Milltown Dam removal in Western Montana over a five year period. It was determined that ANN modeling had equal or greater accuracy for groundwater forecasting with far less effort and cost involved.

  8. Time Series Analysis for Forecasting Hospital Census: Application to the Neonatal Intensive Care Unit.

    Science.gov (United States)

    Capan, Muge; Hoover, Stephen; Jackson, Eric V; Paul, David; Locke, Robert

    2016-01-01

    Accurate prediction of future patient census in hospital units is essential for patient safety, health outcomes, and resource planning. Forecasting census in the Neonatal Intensive Care Unit (NICU) is particularly challenging due to limited ability to control the census and clinical trajectories. The fixed average census approach, using average census from previous year, is a forecasting alternative used in clinical practice, but has limitations due to census variations. Our objectives are to: (i) analyze the daily NICU census at a single health care facility and develop census forecasting models, (ii) explore models with and without patient data characteristics obtained at the time of admission, and (iii) evaluate accuracy of the models compared with the fixed average census approach. We used five years of retrospective daily NICU census data for model development (January 2008 - December 2012, N=1827 observations) and one year of data for validation (January - December 2013, N=365 observations). Best-fitting models of ARIMA and linear regression were applied to various 7-day prediction periods and compared using error statistics. The census showed a slightly increasing linear trend. Best fitting models included a non-seasonal model, ARIMA(1,0,0), seasonal ARIMA models, ARIMA(1,0,0)x(1,1,2)7 and ARIMA(2,1,4)x(1,1,2)14, as well as a seasonal linear regression model. Proposed forecasting models resulted on average in 36.49% improvement in forecasting accuracy compared with the fixed average census approach. Time series models provide higher prediction accuracy under different census conditions compared with the fixed average census approach. Presented methodology is easily applicable in clinical practice, can be generalized to other care settings, support short- and long-term census forecasting, and inform staff resource planning.

  9. Support vector machine for day ahead electricity price forecasting

    Science.gov (United States)

    Razak, Intan Azmira binti Wan Abdul; Abidin, Izham bin Zainal; Siah, Yap Keem; Rahman, Titik Khawa binti Abdul; Lada, M. Y.; Ramani, Anis Niza binti; Nasir, M. N. M.; Ahmad, Arfah binti

    2015-05-01

    Electricity price forecasting has become an important part of power system operation and planning. In a pool- based electric energy market, producers submit selling bids consisting in energy blocks and their corresponding minimum selling prices to the market operator. Meanwhile, consumers submit buying bids consisting in energy blocks and their corresponding maximum buying prices to the market operator. Hence, both producers and consumers use day ahead price forecasts to derive their respective bidding strategies to the electricity market yet reduce the cost of electricity. However, forecasting electricity prices is a complex task because price series is a non-stationary and highly volatile series. Many factors cause for price spikes such as volatility in load and fuel price as well as power import to and export from outside the market through long term contract. This paper introduces an approach of machine learning algorithm for day ahead electricity price forecasting with Least Square Support Vector Machine (LS-SVM). Previous day data of Hourly Ontario Electricity Price (HOEP), generation's price and demand from Ontario power market are used as the inputs for training data. The simulation is held using LSSVMlab in Matlab with the training and testing data of 2004. SVM that widely used for classification and regression has great generalization ability with structured risk minimization principle rather than empirical risk minimization. Moreover, same parameter settings in trained SVM give same results that absolutely reduce simulation process compared to other techniques such as neural network and time series. The mean absolute percentage error (MAPE) for the proposed model shows that SVM performs well compared to neural network.

  10. Industrial Production Forecasting with Kalman Filtering

    Science.gov (United States)

    Teodoro, M. Filomena

    2009-09-01

    Under certains conditions commonly used forecast methods reveal themselves inappropriate for the desired result. Thus it is therefore required to resort to other methods deemed more adequate to the desired results. The main purpose of this work is to investigate the suitability of the Kalman filtering as short term forecast method. The work data set was made of qualitative surveys of conjunture and the index of industrial production. Along the work it is studied the application of this method to a set of qualitative and quantitative information. The ultimate objective is the attainment of short term forecast models for the industrial production index of the transforming industry. After the previous treatment of the data, a number of models are estimated and validated. At the end of this process two models are selected: the two better adjusted in accordance with criteria previously established. The next step in process, following the choice of the models, is the repetition of the sellected models estimation using all sample, after which the actual forecast is done. The results obtained with the described methods have shown sufficiently coincident with the estimates supplied by Portuguese National Institute of Statistics for the same period. The application of Kalman filtering to a set of qualitative and quantitative information in the attainment of models of short term forecasts appears quit promising thus.

  11. The ability of a multi-model seasonal forecasting ensemble to forecast the frequency of warm, cold and wet extremes

    Directory of Open Access Journals (Sweden)

    Acacia S. Pepler

    2015-09-01

    Full Text Available Dynamical models are now widely used to provide forecasts of above or below average seasonal mean temperatures and precipitation, with growing interest in their ability to forecast climate extremes on a seasonal time scale. This study assesses the skill of the ENSEMBLES multi-model ensemble to forecast the 90th and 10th percentiles of both seasonal temperature and precipitation, using a number of metrics of ‘extremeness’. Skill is generally similar or slightly lower to that for seasonal means, with skill strongly influenced by the El Niño-Southern Oscillation. As documented in previous studies, much of the skill in forecasting extremes can be related to skill in forecasting the seasonal mean value, with skill for extremes generally lower although still significant. Despite this, little relationship is found between the skill of forecasting the upper and lower tails of the distribution of daily values.

  12. UCERF3: A new earthquake forecast for California's complex fault system

    Science.gov (United States)

    Field, Edward H.; ,

    2015-01-01

    With innovations, fresh data, and lessons learned from recent earthquakes, scientists have developed a new earthquake forecast model for California, a region under constant threat from potentially damaging events. The new model, referred to as the third Uniform California Earthquake Rupture Forecast, or "UCERF" (http://www.WGCEP.org/UCERF3), provides authoritative estimates of the magnitude, location, and likelihood of earthquake fault rupture throughout the state. Overall the results confirm previous findings, but with some significant changes because of model improvements. For example, compared to the previous forecast (Uniform California Earthquake Rupture Forecast 2), the likelihood of moderate-sized earthquakes (magnitude 6.5 to 7.5) is lower, whereas that of larger events is higher. This is because of the inclusion of multifault ruptures, where earthquakes are no longer confined to separate, individual faults, but can occasionally rupture multiple faults simultaneously. The public-safety implications of this and other model improvements depend on several factors, including site location and type of structure (for example, family dwelling compared to a long-span bridge). Building codes, earthquake insurance products, emergency plans, and other risk-mitigation efforts will be updated accordingly. This model also serves as a reminder that damaging earthquakes are inevitable for California. Fortunately, there are many simple steps residents can take to protect lives and property.

  13. NYHOPS Forecast Model Results

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — 3D Marine Nowcast/Forecast System for the New York Bight NYHOPS subdomain. Currents, waves, surface meteorology, and water conditions.

  14. Solar UV forecasts: a randomized trial assessing their impact on adults' sun-protection behavior.

    Science.gov (United States)

    Dixon, Helen G; Hill, David J; Karoly, David J; Jolley, Damien J; Aden, Said M

    2007-06-01

    This study examined the effectiveness of solar UV forecasts and supporting communications in assisting adults to protect themselves from excessive weekend sun exposure. The study was conducted in Australia, where 557 adult participants with workplace e-mail and Internet access were randomly allocated to one of three weather forecast conditions: standard forecast (no UV), standard forecast + UV, standard forecast + UV + sun-protection messages. From late spring through summer and early autumn, they were e-mailed weekend weather forecasts late in the working week. Each Monday they were e-mailed a prompt to complete a Web-based questionnaire to report sun-related behavior and any sunburn experienced during the previous weekend. There were no significant differences between weather forecast conditions in reported hat use, sunscreen use, sun avoidance, or sunburn. Results indicate that provision of solar-UV forecasts in weather forecasts did not promote markedly enhanced personal sun-protection practices among the adults surveyed.

  15. Earthquake number forecasts testing

    Science.gov (United States)

    Kagan, Yan Y.

    2017-10-01

    We study the distributions of earthquake numbers in two global earthquake catalogues: Global Centroid-Moment Tensor and Preliminary Determinations of Epicenters. The properties of these distributions are especially required to develop the number test for our forecasts of future seismic activity rate, tested by the Collaboratory for Study of Earthquake Predictability (CSEP). A common assumption, as used in the CSEP tests, is that the numbers are described by the Poisson distribution. It is clear, however, that the Poisson assumption for the earthquake number distribution is incorrect, especially for the catalogues with a lower magnitude threshold. In contrast to the one-parameter Poisson distribution so widely used to describe earthquake occurrences, the negative-binomial distribution (NBD) has two parameters. The second parameter can be used to characterize the clustering or overdispersion of a process. We also introduce and study a more complex three-parameter beta negative-binomial distribution. We investigate the dependence of parameters for both Poisson and NBD distributions on the catalogue magnitude threshold and on temporal subdivision of catalogue duration. First, we study whether the Poisson law can be statistically rejected for various catalogue subdivisions. We find that for most cases of interest, the Poisson distribution can be shown to be rejected statistically at a high significance level in favour of the NBD. Thereafter, we investigate whether these distributions fit the observed distributions of seismicity. For this purpose, we study upper statistical moments of earthquake numbers (skewness and kurtosis) and compare them to the theoretical values for both distributions. Empirical values for the skewness and the kurtosis increase for the smaller magnitude threshold and increase with even greater intensity for small temporal subdivision of catalogues. The Poisson distribution for large rate values approaches the Gaussian law, therefore its skewness

  16. 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.

  17. Seasonal UK hydrological forecasts using rainfall forecasts - what level of skill?

    Science.gov (United States)

    Bell, Victoria; Davies, Helen; Kay, Alison; Scaife, Adam

    2017-04-01

    Skilful winter seasonal predictions for the North Atlantic circulation and Northern Europe, including the UK have now been demonstrated and the potential for seasonal hydrological forecasting in the UK is now being explored. The Hydrological Outlook UK (HOUK: www.hydoutuk.net) is the first operational hydrological forecast system for the UK that delivers monthly outlooks of the water situation for both river flow and groundwater levels. The output from the HOUK are publicly available and used each month by government agencies, practitioners and academics alongside other sources of information such as flood warnings and meteorological forecasts. The HOUK brings together information on current and forecast weather conditions, and river flows, and uses several modelling approaches to explore possible future hydrological conditions. One of the techniques combines ensembles of monthly-resolution seasonal rainfall forecasts provided by the Met Office GloSea5 forecast system with hydrological modelling tools to provide estimates of river flows up to a few months ahead. The approach combines a high resolution, spatially distributed hydrological initial condition (HIC) provided by a hydrological model (Grid-to-Grid) driven by weather observations up to the forecast time origin. Considerable efforts have been made to accommodate the temporal and spatial resolution of the GloSea5 rainfall forecasts (monthly time-step and national-scale) in a spatially distributed forecasting system, leading to the development of a monthly resolution water balance model (WBM) to forecast regional mean river flows for the next 1 and 3 months ahead. The work presented here provides the first assessment of the skill in the HOUK national-scale flow forecasts using an ensemble of rainfall forecasts (hindcasts) from the GloSea5 model (1996 to 2009). The skill in the combined modelling system has been assessed for different seasons and regions of Britain, and compared to what might be achieved using

  18. New Approach To Hour-By-Hour Weather Forecast

    Science.gov (United States)

    Liao, Q. Q.; Wang, B.

    2017-12-01

    Fine hourly forecast in single station weather forecast is required in many human production and life application situations. Most previous MOS (Model Output Statistics) which used a linear regression model are hard to solve nonlinear natures of the weather prediction and forecast accuracy has not been sufficient at high temporal resolution. This study is to predict the future meteorological elements including temperature, precipitation, relative humidity and wind speed in a local region over a relatively short period of time at hourly level. By means of hour-to-hour NWP (Numeral Weather Prediction)meteorological field from Forcastio (https://darksky.net/dev/docs/forecast) and real-time instrumental observation including 29 stations in Yunnan and 3 stations in Tianjin of China from June to October 2016, predictions are made of the 24-hour hour-by-hour ahead. This study presents an ensemble approach to combine the information of instrumental observation itself and NWP. Use autoregressive-moving-average (ARMA) model to predict future values of the observation time series. Put newest NWP products into the equations derived from the multiple linear regression MOS technique. Handle residual series of MOS outputs with autoregressive (AR) model for the linear property presented in time series. Due to the complexity of non-linear property of atmospheric flow, support vector machine (SVM) is also introduced . Therefore basic data quality control and cross validation makes it able to optimize the model function parameters , and do 24 hours ahead residual reduction with AR/SVM model. Results show that AR model technique is better than corresponding multi-variant MOS regression method especially at the early 4 hours when the predictor is temperature. MOS-AR combined model which is comparable to MOS-SVM model outperform than MOS. Both of their root mean square error and correlation coefficients for 2 m temperature are reduced to 1.6 degree Celsius and 0.91 respectively. The

  19. Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations

    Energy Technology Data Exchange (ETDEWEB)

    Hoff, Thomas Hoff [Clean Power Research, L.L.C., Napa, CA (United States); Kankiewicz, Adam [Clean Power Research, L.L.C., Napa, CA (United States)

    2016-02-26

    Four major research objectives were completed over the course of this study. Three of the objectives were to evaluate three, new, state-of-the-art solar irradiance forecasting models. The fourth objective was to improve the California Independent System Operator’s (ISO) load forecasts by integrating behind-the-meter (BTM) PV forecasts. The three, new, state-of-the-art solar irradiance forecasting models included: the infrared (IR) satellite-based cloud motion vector (CMV) model; the WRF-SolarCA model and variants; and the Optimized Deep Machine Learning (ODML)-training model. The first two forecasting models targeted known weaknesses in current operational solar forecasts. They were benchmarked against existing operational numerical weather prediction (NWP) forecasts, visible satellite CMV forecasts, and measured PV plant power production. IR CMV, WRF-SolarCA, and ODML-training forecasting models all improved the forecast to a significant degree. Improvements varied depending on time of day, cloudiness index, and geographic location. The fourth objective was to demonstrate that the California ISO’s load forecasts could be improved by integrating BTM PV forecasts. This objective represented the project’s most exciting and applicable gains. Operational BTM forecasts consisting of 200,000+ individual rooftop PV forecasts were delivered into the California ISO’s real-time automated load forecasting (ALFS) environment. They were then evaluated side-by-side with operational load forecasts with no BTM-treatment. Overall, ALFS-BTM day-ahead (DA) forecasts performed better than baseline ALFS forecasts when compared to actual load data. Specifically, ALFS-BTM DA forecasts were observed to have the largest reduction of error during the afternoon on cloudy days. Shorter term 30 minute-ahead ALFS-BTM forecasts were shown to have less error under all sky conditions, especially during the morning time periods when traditional load forecasts often experience their largest

  20. Assessing flood forecast uncertainty with fuzzy arithmetic

    Directory of Open Access Journals (Sweden)

    de Bruyn Bertrand

    2016-01-01

    Full Text Available Providing forecasts for flow rates and water levels during floods have to be associated with uncertainty estimates. The forecast sources of uncertainty are plural. For hydrological forecasts (rainfall-runoff performed using a deterministic hydrological model with basic physics, two main sources can be identified. The first obvious source is the forcing data: rainfall forecast data are supplied in real time by meteorological forecasting services to the Flood Forecasting Service within a range between a lowest and a highest predicted discharge. These two values define an uncertainty interval for the rainfall variable provided on a given watershed. The second source of uncertainty is related to the complexity of the modeled system (the catchment impacted by the hydro-meteorological phenomenon, the number of variables that may describe the problem and their spatial and time variability. The model simplifies the system by reducing the number of variables to a few parameters. Thus it contains an intrinsic uncertainty. This model uncertainty is assessed by comparing simulated and observed rates for a large number of hydro-meteorological events. We propose a method based on fuzzy arithmetic to estimate the possible range of flow rates (and levels of water making a forecast based on possible rainfalls provided by forcing and uncertainty model. The model uncertainty is here expressed as a range of possible values. Both rainfall and model uncertainties are combined with fuzzy arithmetic. This method allows to evaluate the prediction uncertainty range. The Flood Forecasting Service of Oise and Aisne rivers, in particular, monitors the upstream watershed of the Oise at Hirson. This watershed’s area is 310 km2. Its response time is about 10 hours. Several hydrological models are calibrated for flood forecasting in this watershed and use the rainfall forecast. This method presents the advantage to be easily implemented. Moreover, it permits to be carried out

  1. Forecasting Safe or Dangerous Space Weather from HMI Magnetograms

    Science.gov (United States)

    Falconer, David; Barghouty, Abdulnasser F.; Khazanov, Igor; Moore, Ron

    2011-01-01

    We have developed a space-weather forecasting tool using an active-region free-energy proxy that was measured from MDI line-of-sight magnetograms. To develop this forecasting tool (Falconer et al 2011, Space Weather Journal, in press), we used a database of 40,000 MDI magnetograms of 1300 active regions observed by MDI during the previous solar cycle (cycle 23). From each magnetogram we measured our free-energy proxy and for each active region we determined its history of major flare, CME and Solar Particle Event (SPE) production. This database determines from the value of an active region s free-energy proxy the active region s expected rate of production of 1) major flares, 2) CMEs, 3) fast CMEs, and 4) SPEs during the next few days. This tool was delivered to NASA/SRAG in 2010. With MDI observations ending, we have to be able to use HMI magnetograms instead of MDI magnetograms. One of the difficulties is that the measured value of the free-energy proxy is sensitive to the spatial resolution of the measured magnetogram: the 0.5 /pixel resolution of HMI gives a different value for the free-energy proxy than the 2 /pixels resolution of MDI. To use our MDI-database forecasting curves until a comparably large HMI database is accumulated, we smooth HMI line-of-sight magnetograms to MDI resolution, so that we can use HMI to find the value of the free-energy proxy that MDI would have measured, and then use the forecasting curves given by the MDI database. The new version for use with HMI magnetograms was delivered to NASA/SRAG (March 2011). It can also use GONG magnetograms, as a backup.

  2. Automated time series forecasting for biosurveillance.

    Science.gov (United States)

    Burkom, Howard S; Murphy, Sean Patrick; Shmueli, Galit

    2007-09-30

    For robust detection performance, traditional control chart monitoring for biosurveillance is based on input data free of trends, day-of-week effects, and other systematic behaviour. Time series forecasting methods may be used to remove this behaviour by subtracting forecasts from observations to form residuals for algorithmic input. We describe three forecast methods and compare their predictive accuracy on each of 16 authentic syndromic data streams. The methods are (1) a non-adaptive regression model using a long historical baseline, (2) an adaptive regression model with a shorter, sliding baseline, and (3) the Holt-Winters method for generalized exponential smoothing. Criteria for comparing the forecasts were the root-mean-square error, the median absolute per cent error (MedAPE), and the median absolute deviation. The median-based criteria showed best overall performance for the Holt-Winters method. The MedAPE measures over the 16 test series averaged 16.5, 11.6, and 9.7 for the non-adaptive regression, adaptive regression, and Holt-Winters methods, respectively. The non-adaptive regression forecasts were degraded by changes in the data behaviour in the fixed baseline period used to compute model coefficients. The mean-based criterion was less conclusive because of the effects of poor forecasts on a small number of calendar holidays. The Holt-Winters method was also most effective at removing serial autocorrelation, with most 1-day-lag autocorrelation coefficients below 0.15. The forecast methods were compared without tuning them to the behaviour of individual series. We achieved improved predictions with such tuning of the Holt-Winters method, but practical use of such improvements for routine surveillance will require reliable data classification methods.

  3. Multicomponent ensemble models to forecast induced seismicity

    Science.gov (United States)

    Király-Proag, E.; Gischig, V.; Zechar, J. D.; Wiemer, S.

    2018-01-01

    In recent years, human-induced seismicity has become a more and more relevant topic due to its economic and social implications. Several models and approaches have been developed to explain underlying physical processes or forecast induced seismicity. They range from simple statistical models to coupled numerical models incorporating complex physics. We advocate the need for forecast testing as currently the best method for ascertaining if models are capable to reasonably accounting for key physical governing processes—or not. Moreover, operational forecast models are of great interest to help on-site decision-making in projects entailing induced earthquakes. We previously introduced a standardized framework following the guidelines of the Collaboratory for the Study of Earthquake Predictability, the Induced Seismicity Test Bench, to test, validate, and rank induced seismicity models. In this study, we describe how to construct multicomponent ensemble models based on Bayesian weightings that deliver more accurate forecasts than individual models in the case of Basel 2006 and Soultz-sous-Forêts 2004 enhanced geothermal stimulation projects. For this, we examine five calibrated variants of two significantly different model groups: (1) Shapiro and Smoothed Seismicity based on the seismogenic index, simple modified Omori-law-type seismicity decay, and temporally weighted smoothed seismicity; (2) Hydraulics and Seismicity based on numerically modelled pore pressure evolution that triggers seismicity using the Mohr-Coulomb failure criterion. We also demonstrate how the individual and ensemble models would perform as part of an operational Adaptive Traffic Light System. Investigating seismicity forecasts based on a range of potential injection scenarios, we use forecast periods of different durations to compute the occurrence probabilities of seismic events M ≥ 3. We show that in the case of the Basel 2006 geothermal stimulation the models forecast hazardous levels

  4. Objective Lightning Probability Forecasts for East-Central Florida Airports

    Science.gov (United States)

    Crawford, Winfred C.

    2013-01-01

    The forecasters at the National Weather Service in Melbourne, FL, (NWS MLB) identified a need to make more accurate lightning forecasts to help alleviate delays due to thunderstorms in the vicinity of several commercial airports in central Florida at which they are responsible for issuing terminal aerodrome forecasts. Such forecasts would also provide safer ground operations around terminals, and would be of value to Center Weather Service Units serving air traffic controllers in Florida. To improve the forecast, the AMU was tasked to develop an objective lightning probability forecast tool for the airports using data from the National Lightning Detection Network (NLDN). The resulting forecast tool is similar to that developed by the AMU to support space launch operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) for use by the 45th Weather Squadron (45 WS) in previous tasks (Lambert and Wheeler 2005, Lambert 2007). The lightning probability forecasts are valid for the time periods and areas needed by the NWS MLB forecasters in the warm season months, defined in this task as May-September.

  5. Verification of space weather forecasts at the UK Met Office

    Science.gov (United States)

    Bingham, S.; Sharpe, M.; Jackson, D.; Murray, S.

    2017-12-01

    The UK Met Office Space Weather Operations Centre (MOSWOC) has produced space weather guidance twice a day since its official opening in 2014. Guidance includes 4-day probabilistic forecasts of X-ray flares, geomagnetic storms, high-energy electron events and high-energy proton events. Evaluation of such forecasts is important to forecasters, stakeholders, model developers and users to understand the performance of these forecasts and also strengths and weaknesses to enable further development. Met Office terrestrial near real-time verification systems have been adapted to provide verification of X-ray flare and geomagnetic storm forecasts. Verification is updated daily to produce Relative Operating Characteristic (ROC) curves and Reliability diagrams, and rolling Ranked Probability Skill Scores (RPSSs) thus providing understanding of forecast performance and skill. Results suggest that the MOSWOC issued X-ray flare forecasts are usually not statistically significantly better than a benchmark climatological forecast (where the climatology is based on observations from the previous few months). By contrast, the issued geomagnetic storm activity forecast typically performs better against this climatological benchmark.

  6. Drought Monitoring and Forecasting Using the Princeton/U Washington National Hydrologic Forecasting System

    Science.gov (United States)

    Wood, E. F.; Yuan, X.; Roundy, J. K.; Lettenmaier, D. P.; Mo, K. C.; Xia, Y.; Ek, M. B.

    2011-12-01

    Extreme hydrologic events in the form of droughts or floods are a significant source of social and economic damage in many parts of the world. Having sufficient warning of extreme events allows managers to prepare for and reduce the severity of their impacts. A hydrologic forecast system can give seasonal predictions that can be used by mangers to make better decisions; however there is still much uncertainty associated with such a system. Therefore it is important to understand the forecast skill of the system before transitioning to operational usage. Seasonal reforecasts (1982 - 2010) from the NCEP Climate Forecast System (both version 1 (CFS) and version 2 (CFSv2), Climate Prediction Center (CPC) outlooks and the European Seasonal Interannual Prediction (EUROSIP) system, are assessed for forecasting skill in drought prediction across the U.S., both singularly and as a multi-model system The Princeton/U Washington national hydrologic monitoring and forecast system is being implemented at NCEP/EMC via their Climate Test Bed as the experimental hydrological forecast system to support U.S. operational drought prediction. Using our system, the seasonal forecasts are biased corrected, downscaled and used to drive the Variable Infiltration Capacity (VIC) land surface model to give seasonal forecasts of hydrologic variables with lead times of up to six months. Results are presented for a number of events, with particular focus on the Apalachicola-Chattahoochee-Flint (ACF) River Basin in the South Eastern United States, which has experienced a number of severe droughts in recent years and is a pilot study basin for the National Integrated Drought Information System (NIDIS). The performance of the VIC land surface model is evaluated using observational forcing when compared to observed streamflow. The effectiveness of the forecast system to predict streamflow and soil moisture is evaluated when compared with observed streamflow and modeled soil moisture driven by

  7. Accuracy of annual prescription drug expenditure forecasts in AJHP.

    Science.gov (United States)

    Hartke, Patricia L; Vermeulen, Lee C; Hoffman, James M; Shah, Nilay D; Doloresco, Fred; Suda, Katie J; Li, Edward C; Matusiak, Linda M; Hunkler, Robert J; Schumock, Glen T

    2015-10-01

    The accuracy of the forecasts of drug expenditures in nonfederal hospitals and clinics published annually in the American Journal of Health-System Pharmacy (AJHP) relative to the accuracy of forecasts produced by the Centers for Medicare and Medicaid Services (CMS) was evaluated. AJHP-published forecasts of drug expenditure growth for nonfederal hospitals (for the years 2003 through 2013) and clinics (for the years 2004 through 2013) were compared with data on actual growth. Data on actual and projected growth published by CMS were analyzed for the years 2003 through 2012. The mean absolute error and directional accuracy of the forecasts published in AJHP for nonfederal hospitals and clinics and the CMS forecasts were determined and compared. Actual spending growth was within the range of the forecast published in AJHP for 2 of 11 years for nonfederal hospitals and for 3 of 10 years for clinics; the forecasts for nonfederal hospitals and clinics were directionally accurate 27.3% and 60.0% of the time, respectively. The mean absolute errors of the AJHP-published drug expenditure forecasts for the nonfederal hospital and clinic sectors were 2.0 and 4.7 percentage points, respectively. The CMS forecasts of overall drug spending were directionally accurate 70% of the time, and the mean absolute error (2.2 percentage points) was not statistically different from that of either sector forecast published in AJHP. The annual drug expenditure forecasts published in AJHP have been reasonably accurate for predicting growth in prescription expenditures when compared with other available drug expenditure forecasts. Copyright © 2015 by the American Society of Health-System Pharmacists, Inc. All rights reserved.

  8. Regional Model Nesting Within GFS Daily Forecasts Over West Africa

    Science.gov (United States)

    Druyan, Leonard M.; Fulakeza, Matthew; Lonergan, Patrick; Worrell, Ruben

    2010-01-01

    The study uses the RM3, the regional climate model at the Center for Climate Systems Research of Columbia University and the NASA/Goddard Institute for Space Studies (CCSR/GISS). The paper evaluates 30 48-hour RM3 weather forecasts over West Africa during September 2006 made on a 0.5 grid nested within 1 Global Forecast System (GFS) global forecasts. September 2006 was the Special Observing Period #3 of the African Monsoon Multidisciplinary Analysis (AMMA). Archived GFS initial conditions and lateral boundary conditions for the simulations from the US National Weather Service, National Oceanographic and Atmospheric Administration were interpolated four times daily. Results for precipitation forecasts are validated against Tropical Rainfall Measurement Mission (TRMM) satellite estimates and data from the Famine Early Warning System (FEWS), which includes rain gauge measurements, and forecasts of circulation are compared to reanalysis 2. Performance statistics for the precipitation forecasts include bias, root-mean-square errors and spatial correlation coefficients. The nested regional model forecasts are compared to GFS forecasts to gauge whether nesting provides additional realistic information. They are also compared to RM3 simulations driven by reanalysis 2, representing high potential skill forecasts, to gauge the sensitivity of results to lateral boundary conditions. Nested RM3/GFS forecasts generate excessive moisture advection toward West Africa, which in turn causes prodigious amounts of model precipitation. This problem is corrected by empirical adjustments in the preparation of lateral boundary conditions and initial conditions. The resulting modified simulations improve on the GFS precipitation forecasts, achieving time-space correlations with TRMM of 0.77 on the first day and 0.63 on the second day. One realtime RM3/GFS precipitation forecast made at and posted by the African Centre of Meteorological Application for Development (ACMAD) in Niamey, Niger

  9. 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)

  10. Air Quality Forecasts Using the NASA GEOS Model: A Unified Tool from Local to Global Scales

    Science.gov (United States)

    Knowland, E. Emma; Keller, Christoph; Nielsen, J. Eric; Orbe, Clara; Ott, Lesley; Pawson, Steven; Saunders, Emily; Duncan, Bryan; Cook, Melanie; Liu, Junhua; hide

    2017-01-01

    We provide an introduction to a new high-resolution (0.25 degree) global composition forecast produced by NASA's Global Modeling and Assimilation office. The NASA Goddard Earth Observing System version 5 (GEOS-5) model has been expanded to provide global near-real-time forecasts of atmospheric composition at a horizontal resolution of 0.25 degrees (approximately 25 km). Previously, this combination of detailed chemistry and resolution was only provided by regional models. This system combines the operational GEOS-5 weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 11) to provide detailed chemical analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). The resolution of the forecasts is the highest resolution compared to current, publically-available global composition forecasts. Evaluation and validation of modeled trace gases and aerosols compared to surface and satellite observations will be presented for constituents relative to health air quality standards. Comparisons of modeled trace gases and aerosols against satellite observations show that the model produces realistic concentrations of atmospheric constituents in the free troposphere. Model comparisons against surface observations highlight the model's capability to capture the diurnal variability of air pollutants under a variety of meteorological conditions. The GEOS-5 composition forecasting system offers a new tool for scientists and the public health community, and is being developed jointly with several government and non-profit partners. Potential applications include air quality warnings, flight campaign planning and exposure studies using the archived analysis fields.

  11. Layered Ensemble Architecture for Time Series Forecasting.

    Science.gov (United States)

    Rahman, Md Mustafizur; Islam, Md Monirul; Murase, Kazuyuki; Yao, Xin

    2016-01-01

    Time series forecasting (TSF) has been widely used in many application areas such as science, engineering, and finance. The phenomena generating time series are usually unknown and information available for forecasting is only limited to the past values of the series. It is, therefore, necessary to use an appropriate number of past values, termed lag, for forecasting. This paper proposes a layered ensemble architecture (LEA) for TSF problems. Our LEA consists of two layers, each of which uses an ensemble of multilayer perceptron (MLP) networks. While the first ensemble layer tries to find an appropriate lag, the second ensemble layer employs the obtained lag for forecasting. Unlike most previous work on TSF, the proposed architecture considers both accuracy and diversity of the individual networks in constructing an ensemble. LEA trains different networks in the ensemble by using different training sets with an aim of maintaining diversity among the networks. However, it uses the appropriate lag and combines the best trained networks to construct the ensemble. This indicates LEAs emphasis on accuracy of the networks. The proposed architecture has been tested extensively on time series data of neural network (NN)3 and NN5 competitions. It has also been tested on several standard benchmark time series data. In terms of forecasting accuracy, our experimental results have revealed clearly that LEA is better than other ensemble and nonensemble methods.

  12. A Hybrid Model for Forecasting Sales in Turkish Paint Industry

    Directory of Open Access Journals (Sweden)

    Alp Ustundag

    2009-12-01

    Full Text Available Sales forecasting is important for facilitating effective and efficient allocation of scarce resources. However, how to best model and forecast sales has been a long-standing issue. There is no best forecasting method that is applicable in all circumstances. Therefore, confidence in the accuracy of sales forecasts is achieved by corroborating the results using two or more methods. This paper proposes a hybrid forecasting model that uses an artificial intelligence method (AI with multiple linear regression (MLR to predict product sales for the largest Turkish paint producer. In the hybrid model, three different AI methods, fuzzy rule-based system (FRBS, artificial neural network (ANN and adaptive neuro fuzzy network (ANFIS, are used and compared to each other. The results indicate that FRBS yields better forecasting accuracy in terms of root mean squared error (RMSE and mean absolute percentage error (MAPE.

  13. Seasonal rainfall prediction skill over South Africa: one- versus two-tiered forecasting systems

    CSIR Research Space (South Africa)

    Landman, WA

    2012-04-01

    Full Text Available Forecast performance by coupled ocean–atmosphere or one-tiered models predicting seasonal rainfall totals over South Africa is compared with forecasts produced by computationally less demanding two-tiered systems where prescribed sea surface...

  14. Space Weather Forecasting at IZMIRAN

    Science.gov (United States)

    Gaidash, S. P.; Belov, A. V.; Abunina, M. A.; Abunin, A. A.

    2017-12-01

    Since 1998, the Institute of Terrestrial Magnetism, Ionosphere, and Radio Wave Propagation (IZMIRAN) has had an operating heliogeophysical service—the Center for Space Weather Forecasts. This center transfers the results of basic research in solar-terrestrial physics into daily forecasting of various space weather parameters for various lead times. The forecasts are promptly available to interested consumers. This article describes the center and the main types of forecasts it provides: solar and geomagnetic activity, magnetospheric electron fluxes, and probabilities of proton increases. The challenges associated with the forecasting of effects of coronal mass ejections and coronal holes are discussed. Verification data are provided for the center's forecasts.

  15. Forecasting with nonlinear time series models

    DEFF Research Database (Denmark)

    Kock, Anders Bredahl; Teräsvirta, Timo

    applied to economic fore- casting problems, is briefly highlighted. A number of large published studies comparing macroeconomic forecasts obtained using different time series models are discussed, and the paper also contains a small simulation study comparing recursive and direct forecasts in a partic......In this paper, nonlinear models are restricted to mean nonlinear parametric models. Several such models popular in time series econo- metrics are presented and some of their properties discussed. This in- cludes two models based on universal approximators: the Kolmogorov- Gabor polynomial model...

  16. Financial Analysts’ Forecasts

    DEFF Research Database (Denmark)

    Stæhr, Simone

    in the decision making and the magnitude of these constraints does sometimes vary with personal traits. Therefore, to the extent that financial analysts are subjects to behavioral biases their outputs to the investors are likely to be biased by their interpretation of information. Because investors need accuracy....... The primary focus is on financial analysts in the task of conducting earnings forecasts while a secondary focus is on investors’ abilities to interpret and make use of these forecasts. Simply put, financial analysts can be seen as information intermediators receiving inputs to their analyses from firm...... management and providing outputs to the investors. Amongst various outputs from the analysts are forecasts of earnings. According to decision theories mostly from the literature in psychology all humans are affected by cognitive constraints to some degree. These constraints may lead to unintentional biases...

  17. Forecast Mekong: navigating changing waters

    Science.gov (United States)

    Powell, Janine

    2011-01-01

    The U.S. Geological Survey (USGS) is using research and data from the Mekong River Delta in Southeast Asia to compare restoration, conservation, and management efforts there with those done in other major river deltas, such as the Mississippi River Delta in the United States. The project provides a forum to engage regional partners in the Mekong Basin countries to share data and support local research efforts. Ultimately, Forecast Mekong will lead to more informed decisions about how to make the Mekong and Mississippi Deltas resilient in the face of climate change, economic stresses, and other impacts.

  18. Testing the data assimilation technique for short-term wind forecast in the PBL: a case study

    Science.gov (United States)

    Avolio, E.; Federico, S.; Sempreviva, A. M.; Calidonna, C. R.; Courtney, M.

    2012-04-01

    In this contribution we show the results of using a data assimilation technique to improve the short-term wind forecast at a site in northern Europe. The assimilation technique is a simple four-dimensional nudging and, for this purpose, we set-up a version of the Regional Atmospheric Modelling System. The nudging technique consists of adding an extra-tendency term, to the prognostic equations of the zonal and meridional wind components, which forces the variable toward the observations. dφm- (φobs -φm-) dt = τ f(r) (1) where φmis model variable (zonal or meridional wind component), φobs is the observation, τ is relaxation time scale (900 s), f(r) is a Gaussian function f(r) = e0-(r/r)Λ2 , and r0=50 km. The method was applied in Denmark where suitable observations were available at the Danish National Test Station for Large Wind Turbines, located at Høvsøre (Western Jutland, Denmark), and refer to the measurements of vertical wind profiles; the instrument is the WINDCUBE Doppler LIDAR. Data were available every 10 minutes at the following levels: 40 m, 60 m, 80 m, 100 m, 116 m, 130 m, 160 m, 200 m, 250 m and 300 m. The data represent the average of the measurement for the previous 10 minutes. Only data available at the 00 minutes of each hour were considered in this study. The RAMS model is set-up with four nested grids. The fourth grid has 1 km horizontal resolution and is centred over the site. Model levels do not coincide with the measurement levels, and, to assimilate and to verify the forecast, the observations were linearly interpolated to the model levels. The physical configuration of the model is the one adopted for operational forecast over the Calabria Region in South Italy. In order to show the potential impact of the nudging technique, we run the model in two different configurations: (a) a simple forecast and (b) an analysis-forecast run. The runs duration is twenty-four hours for both configurations. For each configuration, simulations were

  19. Spatial load forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Willis, H.L.; Engel, M.V.; Buri, M.J.

    1995-04-01

    The reliability, efficiency, and economy of a power delivery system depend mainly on how well its substations, transmission lines, and distribution feeders are located within the utility service area, and how well their capacities match power needs in their respective localities. Often, utility planners are forced to commit to sites, rights of way, and equipment capacities year in advance. A necessary element of effective expansion planning is a forecast of where and how much demand must be served by the future T and D system. This article reports that a three-stage method forecasts with accuracy and detail, allowing meaningful determination of sties and sizes for future substation, transmission, and distribution facilities.

  20. Forecast of auroral activity

    International Nuclear Information System (INIS)

    Lui, A.T.Y.

    2004-01-01

    A new technique is developed to predict auroral activity based on a sample of over 9000 auroral sites identified in global auroral images obtained by an ultraviolet imager on the NASA Polar satellite during a 6-month period. Four attributes of auroral activity sites are utilized in forecasting, namely, the area, the power, and the rates of change in area and power. This new technique is quite accurate, as indicated by the high true skill scores for forecasting three different levels of auroral dissipation during the activity lifetime. The corresponding advanced warning time ranges from 22 to 79 min from low to high dissipation levels

  1. COMPAR

    International Nuclear Information System (INIS)

    Kuefner, K.

    1976-01-01

    COMPAR works on FORTRAN arrays with four indices: A = A(i,j,k,l) where, for each fixed k 0 ,l 0 , only the 'plane' [A(i,j,k 0 ,l 0 ), i = 1, isub(max), j = 1, jsub(max)] is held in fast memory. Given two arrays A, B of this type COMPAR has the capability to 1) re-norm A and B ind different ways; 2) calculate the deviations epsilon defined as epsilon(i,j,k,l): =[A(i,j,k,l) - B(i,j,k,l)] / GEW(i,j,k,l) where GEW (i,j,k,l) may be chosen in three different ways; 3) calculate mean, standard deviation and maximum in the array epsilon (by several intermediate stages); 4) determine traverses in the array epsilon; 5) plot these traverses by a printer; 6) simplify plots of these traverses by the PLOTEASY-system by creating input data blocks for this system. The main application of COMPAR is given (so far) by the comparison of two- and three-dimensional multigroup neutron flux-fields. (orig.) [de

  2. Combining forecasts in short term load forecasting: Empirical ...

    Indian Academy of Sciences (India)

    We present an empirical analysis to show that combination of short term load forecasts leads to better accuracy. We also discuss other aspects of combination, i.e.,distribution of weights, effect of variation in the historical window and distribution of forecast errors. The distribution of forecast errors is analyzed in order to get a ...

  3. 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...

  4. Probabilistic drought intensification forecasts using temporal patterns of satellite-derived drought indicators

    Science.gov (United States)

    Park, Sumin; Im, Jungho; Park, Seonyeong

    2016-04-01

    A drought occurs when the condition of below-average precipitation in a region continues, resulting in prolonged water deficiency. A drought can last for weeks, months or even years, so can have a great influence on various ecosystems including human society. In order to effectively reduce agricultural and economic damage caused by droughts, drought monitoring and forecasts are crucial. Drought forecast research is typically conducted using in situ observations (or derived indices such as Standardized Precipitation Index (SPI)) and physical models. Recently, satellite remote sensing has been used for short term drought forecasts in combination with physical models. In this research, drought intensification was predicted using satellite-derived drought indices such as Normalized Difference Drought Index (NDDI), Normalized Multi-band Drought Index (NMDI), and Scaled Drought Condition Index (SDCI) generated from Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) products over the Korean Peninsula. Time series of each drought index at the 8 day interval was investigated to identify drought intensification patterns. Drought condition at the previous time step (i.e., 8 days before) and change in drought conditions between two previous time steps (e.g., between 16 days and 8 days before the time step to forecast) Results show that among three drought indices, SDCI provided the best performance to predict drought intensification compared to NDDI and NMDI through qualitative assessment. When quantitatively compared with SPI, SDCI showed a potential to be used for forecasting short term drought intensification. Finally this research provided a SDCI-based equation to predict short term drought intensification optimized over the Korean Peninsula.

  5. Improving Artificial Neural Network Forecasts with Kalman Filtering ...

    African Journals Online (AJOL)

    In this paper, we examine the use of the artificial neural network method as a forecasting technique in financial time series and the application of a Kalman filter algorithm to improve the accuracy of the model. Forecasting accuracy criteria are used to compare the two models over different set of data from different companies ...

  6. forecasting in one-dimensional and generalized integrated ...

    African Journals Online (AJOL)

    forecast when compared with the real series but forecast with generalized integrated autoregressive bilinear model is ... proposed one-dimensional integrated autoregressive bilinear time series model that could achieve stationary for all non linear time series. Also ..... Bilinear Moving Average Vector Models. Advances in ...

  7. Institutional Forecasting: The Performance of Thin Virtual Stock Markets

    NARCIS (Netherlands)

    G.H. van Bruggen (Gerrit); M. Spann (Martin); G.L. Lilien (Gary); B. Skiera (Bernd)

    2006-01-01

    textabstractWe study the performance of Virtual Stock Markets (VSMs) in an institutional forecasting environment. We compare VSMs to the Combined Judgmental Forecast (CJF) and the Key Informant (KI) approach. We find that VSMs can be effectively applied in an environment with a small number of

  8. CDM Convective Forecast Planning guidance

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The CDM Convective Forecast Planning (CCFP) guidance product provides a foreast of en-route aviation convective hazards. The forecasts are updated every 2 hours and...

  9. Battlescale Forecast Model Sensitivity Study

    National Research Council Canada - National Science Library

    Sauter, Barbara

    2003-01-01

    .... Changes to the surface observations used in the Battlescale Forecast Model initialization led to no significant changes in the resulting forecast values of temperature, relative humidity, wind speed, or wind direction...

  10. Challenges of operational river forecasting

    NARCIS (Netherlands)

    Pagano, T.C.; Wood, A.W.; Ramos, M.H.; Cloke, H.L.; Pappenbreger, F.; Verkade, J.S.

    2014-01-01

    Skillful and timely streamflow forecasts are critically important to water managers and emergency protection services. To provide these forecasts, hydrologists must predict the behavior of complex coupled human–natural systems using incomplete and uncertain information and imperfect models.

  11. Evaluating Forecasts, Narratives and Policy Using a Test of Invariance

    Directory of Open Access Journals (Sweden)

    Jennifer L. Castle

    2017-09-01

    Full Text Available Economic policy agencies produce forecasts with accompanying narratives, and base policy changes on the resulting anticipated developments in the target variables. Systematic forecast failure, defined as large, persistent deviations of the outturns from the numerical forecasts, can make the associated narrative false, which would in turn question the validity of the entailed policy implementation. We establish when systematic forecast failure entails failure of the accompanying narrative, which we call forediction failure, and when that in turn implies policy invalidity. Most policy regime changes involve location shifts, which can induce forediction failure unless the policy variable is super exogenous in the policy model. We propose a step-indicator saturation test to check in advance for invariance to policy changes. Systematic forecast failure, or a lack of invariance, previously justified by narratives reveals such stories to be economic fiction.

  12. Are demand forecasting techniques applicable to libraries?

    OpenAIRE

    Sridhar, M. S.

    1984-01-01

    Examines the nature and limitations of demand forecasting, discuses plausible methods of forecasting demand for information, suggests some useful hints for demand forecasting and concludes by emphasizing unified approach to demand forecasting.

  13. WOD - Weather On Demand forecasting system

    Science.gov (United States)

    Rognvaldsson, Olafur; Ragnarsson, Logi; Stanislawska, Karolina

    2017-04-01

    The backbone of the Belgingur forecasting system (called WOD - Weather On Demand) is the WRF-Chem atmospheric model, with a number of in-house customisations. Initial and boundary data are taken from the Global Forecasting System, operated by the National Oceanic and Atmospheric Administration (NOAA). Operational forecasts use cycling of a number of parameters, mainly deep soil and surface fields. This is done to minimise spin-up effects and to ensure proper book-keeping of hydrological fields such as snow accumulation and runoff, as well as the constituents of various chemical parameters. The WOD system can be used to create conventional short- to medium-range weather forecasts for any location on the globe. The WOD system can also be used for air quality purposes (e.g. dispersion forecasts from volcanic eruptions) and as a tool to provide input to other modelling systems, such as hydrological models. A wide variety of post-processing options are also available, making WOD an ideal tool for creating highly customised output that can be tailored to the specific needs of individual end-users. The most recent addition to the WOD system is an integrated verification system where forecasts can be compared to surface observations from chosen locations. Forecast visualisation, such as weather charts, meteograms, weather icons and tables, is done via number of web components that can be configured to serve the varying needs of different end-users. The WOD system itself can be installed in an automatic way on hardware running a range of Linux based OS. System upgrades can also be done in semi-automatic fashion, i.e. upgrades and/or bug-fixes can be pushed to the end-user hardware without system downtime. Importantly, the WOD system requires only rudimentary knowledge of the WRF modelling, and the Linux operating systems on behalf of the end-user, making it an ideal NWP tool in locations with limited IT infrastructure.

  14. Establishing a method of short-term rainfall forecasting based on GNSS-derived PWV and its application.

    Science.gov (United States)

    Yao, Yibin; Shan, Lulu; Zhao, Qingzhi

    2017-09-29

    Global Navigation Satellite System (GNSS) can effectively retrieve precipitable water vapor (PWV) with high precision and high-temporal resolution. GNSS-derived PWV can be used to reflect water vapor variation in the process of strong convection weather. By studying the relationship between time-varying PWV and rainfall, it can be found that PWV contents increase sharply before raining. Therefore, a short-term rainfall forecasting method is proposed based on GNSS-derived PWV. Then the method is validated using hourly GNSS-PWV data from Zhejiang Continuously Operating Reference Station (CORS) network of the period 1 September 2014 to 31 August 2015 and its corresponding hourly rainfall information. The results show that the forecasted correct rate can reach about 80%, while the false alarm rate is about 66%. Compared with results of the previous studies, the correct rate is improved by about 7%, and the false alarm rate is comparable. The method is also applied to other three actual rainfall events of different regions, different durations, and different types. The results show that the method has good applicability and high accuracy, which can be used for rainfall forecasting, and in the future study, it can be assimilated with traditional weather forecasting techniques to improve the forecasted accuracy.

  15. Forecasting inbound tourists in Cambodia

    OpenAIRE

    Tanaka, Kiyoyasu

    2016-01-01

    Forecasting tourism demand is crucial for management decisions in the tourism sector. Estimating a vector autoregressive (VAR) model for monthly visitor arrivals disaggregated by three entry points in Cambodia for the years 2006–2015, I forecast the number of arrivals for years 2016 and 2017. The results show that the VAR model fits well with the data on visitor arrivals for each entry point. Ex post forecasting shows that the forecasts closely match the observed data for visitor arrivals, th...

  16. A forecasting model for dengue incidence in the District of Gampaha, Sri Lanka.

    Science.gov (United States)

    Withanage, Gayan P; Viswakula, Sameera D; Nilmini Silva Gunawardena, Y I; Hapugoda, Menaka D

    2018-04-24

    Dengue is one of the major health problems in Sri Lanka causing an enormous social and economic burden to the country. An accurate early warning system can enhance the efficiency of preventive measures. The aim of the study was to develop and validate a simple accurate forecasting model for the District of Gampaha, Sri Lanka. Three time-series regression models were developed using monthly rainfall, rainy days, temperature, humidity, wind speed and retrospective dengue incidences over the period January 2012 to November 2015 for the District of Gampaha, Sri Lanka. Various lag times were analyzed to identify optimum forecasting periods including interactions of multiple lags. The models were validated using epidemiological data from December 2015 to November 2017. Prepared models were compared based on Akaike's information criterion, Bayesian information criterion and residual analysis. The selected model forecasted correctly with mean absolute errors of 0.07 and 0.22, and root mean squared errors of 0.09 and 0.28, for training and validation periods, respectively. There were no dengue epidemics observed in the district during the training period and nine outbreaks occurred during the forecasting period. The proposed model captured five outbreaks and correctly rejected 14 within the testing period of 24 months. The Pierce skill score of the model was 0.49, with a receiver operating characteristic of 86% and 92% sensitivity. The developed weather based forecasting model allows warnings of impending dengue outbreaks and epidemics in advance of one month with high accuracy. Depending upon climatic factors, the previous month's dengue cases had a significant effect on the dengue incidences of the current month. The simple, precise and understandable forecasting model developed could be used to manage limited public health resources effectively for patient management, vector surveillance and intervention programmes in the district.

  17. Forecasters' Objectives and Strategies

    DEFF Research Database (Denmark)

    Marinovic, Iván; Ottaviani, Marco; Sørensen, Peter Norman

    2013-01-01

    This chapter develops a unified modeling framework for analyzing the strategic behavior of forecasters. The theoretical model encompasses reputational objectives, competition for the best accuracy, and bias. Also drawing from the extensive lit- erature on analysts, we review the empirical evidenc...

  18. Housing Price Forecastability

    DEFF Research Database (Denmark)

    Bork, Lasse; Møller, Stig Vinther

    2016-01-01

    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 mo...

  19. Hydrology and flow forecasting

    NARCIS (Netherlands)

    Vrijling, J.K.; Kwadijk, J.; Van Duivendijk, J.; Van Gelder, P.; Pang, H.; Rao, S.Q.; Wang, G.Q.; Huang, X.Q.

    2002-01-01

    We have studied and applied the statistic model (i.e. MMC) and hydrological models to Upper Yellow River. This report introduces the results and some conclusions from the model. The three models, MMC, MWBM and NAM, have be applied in the research area. The forecasted discharge by the three models

  20. Reference class forecasting

    DEFF Research Database (Denmark)

    Flyvbjerg, Bent

    Underbudgettering og budgetoverskridelser forekommer i et flertal af større bygge- og anlægsprojekter. Problemet skyldes optimisme og/eller strategisk misinformation i budgetteringsprocessen. Reference class forecasting (RCF) er en prognosemetode, som er udviklet for at reducere eller eliminere...

  1. Forecasting biodiversity in breeding birds using best practices

    Directory of Open Access Journals (Sweden)

    David J. Harris

    2018-02-01

    Full Text Available Biodiversity forecasts are important for conservation, management, and evaluating how well current models characterize natural systems. While the number of forecasts for biodiversity is increasing, there is little information available on how well these forecasts work. Most biodiversity forecasts are not evaluated to determine how well they predict future diversity, fail to account for uncertainty, and do not use time-series data that captures the actual dynamics being studied. We addressed these limitations by using best practices to explore our ability to forecast the species richness of breeding birds in North America. We used hindcasting to evaluate six different modeling approaches for predicting richness. Hindcasts for each method were evaluated annually for a decade at 1,237 sites distributed throughout the continental United States. All models explained more than 50% of the variance in richness, but none of them consistently outperformed a baseline model that predicted constant richness at each site. The best practices implemented in this study directly influenced the forecasts and evaluations. Stacked species distribution models and “naive” forecasts produced poor estimates of uncertainty and accounting for this resulted in these models dropping in the relative performance compared to other models. Accounting for observer effects improved model performance overall, but also changed the rank ordering of models because it did not improve the accuracy of the “naive” model. Considering the forecast horizon revealed that the prediction accuracy decreased across all models as the time horizon of the forecast increased. To facilitate the rapid improvement of biodiversity forecasts, we emphasize the value of specific best practices in making forecasts and evaluating forecasting methods.

  2. Forecasting biodiversity in breeding birds using best practices

    Science.gov (United States)

    Taylor, Shawn D.; White, Ethan P.

    2018-01-01

    Biodiversity forecasts are important for conservation, management, and evaluating how well current models characterize natural systems. While the number of forecasts for biodiversity is increasing, there is little information available on how well these forecasts work. Most biodiversity forecasts are not evaluated to determine how well they predict future diversity, fail to account for uncertainty, and do not use time-series data that captures the actual dynamics being studied. We addressed these limitations by using best practices to explore our ability to forecast the species richness of breeding birds in North America. We used hindcasting to evaluate six different modeling approaches for predicting richness. Hindcasts for each method were evaluated annually for a decade at 1,237 sites distributed throughout the continental United States. All models explained more than 50% of the variance in richness, but none of them consistently outperformed a baseline model that predicted constant richness at each site. The best practices implemented in this study directly influenced the forecasts and evaluations. Stacked species distribution models and “naive” forecasts produced poor estimates of uncertainty and accounting for this resulted in these models dropping in the relative performance compared to other models. Accounting for observer effects improved model performance overall, but also changed the rank ordering of models because it did not improve the accuracy of the “naive” model. Considering the forecast horizon revealed that the prediction accuracy decreased across all models as the time horizon of the forecast increased. To facilitate the rapid improvement of biodiversity forecasts, we emphasize the value of specific best practices in making forecasts and evaluating forecasting methods. PMID:29441230

  3. 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,

  4. 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.

  5. 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.

  6. Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Cheung, WanYin; Zhang, Jie; Florita, Anthony; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Sun, Qian; Lehman, Brad

    2015-12-08

    Uncertainties associated with solar forecasts present challenges to maintain grid reliability, especially at high solar penetrations. This study aims to quantify the errors associated with the day-ahead solar forecast parameters and the theoretical solar power output for a 51-kW solar power plant in a utility area in the state of Vermont, U.S. Forecasts were generated by three numerical weather prediction (NWP) models, including the Rapid Refresh, the High Resolution Rapid Refresh, and the North American Model, and a machine-learning ensemble model. A photovoltaic (PV) performance model was adopted to calculate theoretical solar power generation using the forecast parameters (e.g., irradiance, cell temperature, and wind speed). Errors of the power outputs were quantified using statistical moments and a suite of metrics, such as the normalized root mean squared error (NRMSE). In addition, the PV model's sensitivity to different forecast parameters was quantified and analyzed. Results showed that the ensemble model yielded forecasts in all parameters with the smallest NRMSE. The NRMSE of solar irradiance forecasts of the ensemble NWP model was reduced by 28.10% compared to the best of the three NWP models. Further, the sensitivity analysis indicated that the errors of the forecasted cell temperature attributed only approximately 0.12% to the NRMSE of the power output as opposed to 7.44% from the forecasted solar irradiance.

  7. A fuzzy inference model for short-term load forecasting

    International Nuclear Information System (INIS)

    Mamlook, Rustum; Badran, Omar; Abdulhadi, Emad

    2009-01-01

    This paper is concerned with the short-term load forecasting (STLF) in power system operations. It provides load prediction for generation scheduling and unit commitment decisions, and therefore precise load forecasting plays an important role in reducing the generation cost and the spinning reserve capacity. Short-term electricity demand forecasting (i.e., the prediction of hourly loads (demand)) is one of the most important tools by which an electric utility/company plans, dispatches the loading of generating units in order to meet system demand. The accuracy of the dispatching system, which is derived from the accuracy of the forecasting algorithm used, will determine the economics of the operation of the power system. The inaccuracy or large error in the forecast simply means that load matching is not optimized and consequently the generation and transmission systems are not being operated in an efficient manner. In the present study, a proposed methodology has been introduced to decrease the forecasted error and the processing time by using fuzzy logic controller on an hourly base. Therefore, it predicts the effect of different conditional parameters (i.e., weather, time, historical data, and random disturbances) on load forecasting in terms of fuzzy sets during the generation process. These parameters are chosen with respect to their priority and importance. The forecasted values obtained by fuzzy method were compared with the conventionally forecasted ones. The results showed that the STLF of the fuzzy implementation have more accuracy and better outcomes

  8. Validation of the Kp Geomagnetic Index Forecast at CCMC

    Science.gov (United States)

    Frechette, B. P.; Mays, M. L.

    2017-12-01

    The Community Coordinated Modeling Center (CCMC) Space Weather Research Center (SWRC) sub-team provides space weather services to NASA robotic mission operators and science campaigns and prototypes new models, forecasting techniques, and procedures. The Kp index is a measure of geomagnetic disturbances for space weather in the magnetosphere such as geomagnetic storms and substorms. In this study, we performed validation on the Newell et al. (2007) Kp prediction equation from December 2010 to July 2017. The purpose of this research is to understand the Kp forecast performance because it's critical for NASA missions to have confidence in the space weather forecast. This research was done by computing the Kp error for each forecast (average, minimum, maximum) and each synoptic period. Then to quantify forecast performance we computed the mean error, mean absolute error, root mean square error, multiplicative bias and correlation coefficient. A contingency table was made for each forecast and skill scores were computed. The results are compared to the perfect score and reference forecast skill score. In conclusion, the skill score and error results show that the minimum of the predicted Kp over each synoptic period from the Newell et al. (2007) Kp prediction equation performed better than the maximum or average of the prediction. However, persistence (reference forecast) outperformed all of the Kp forecasts (minimum, maximum, and average). Overall, the Newell Kp prediction still predicts within a range of 1, even though persistence beats it.

  9. Does money matter in inflation forecasting?

    Science.gov (United States)

    Binner, J. M.; Tino, P.; Tepper, J.; Anderson, R.; Jones, B.; Kendall, G.

    2010-11-01

    This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two nonlinear techniques, namely, recurrent neural networks and kernel recursive least squares regression-techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naïve random walk model. The best models were nonlinear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation. Beyond its economic findings, our study is in the tradition of physicists’ long-standing interest in the interconnections among statistical mechanics, neural networks, and related nonparametric statistical methods, and suggests potential avenues of extension for such studies.

  10. Carbon monoxide concentration forecasting in Santiago, Chile.

    Science.gov (United States)

    Perez, Patricio; Palacios, Rodrigo; Castillo, Alejandro

    2004-08-01

    In the city of Santiago, Chile, air quality is defined in terms of particulate matter with an aerodynamic diameter < or = 10 microm (PM10) concentrations. An air quality forecasting model based on past concentrations of PM10 and meteorological conditions currently is used by the metropolitan agency for the environment, which allows restrictions to emissions to be imposed in advance. This model, however, fails to forecast between 40 and 50% of the days considered to be harmful for the inhabitants every year. Given that a high correlation between particulate matter and carbon monoxide (CO) concentrations is observed at monitoring stations in the city, a model for CO concentration forecasting would be a useful tool to complement information about expected air quality in the city. Here, the results of a neural network-based model aimed to forecast maximum values of the 8-hr moving average of CO concentrations for the next day are presented. Forecasts from the neural network model are compared with those produced with linear regressions. The neural network model seems to leave more room to adjust free parameters with 1-yr data to predict the following year's values. We have worked with 3 yr of data measured at the monitoring station located in the zone with the worst air quality in the city of Santiago, Chile.

  11. Wind forecasting for grid code compliance

    Energy Technology Data Exchange (ETDEWEB)

    Vanitha, V.; Kishore, S.R.N. [Amrita Vishwa Vidyapeetham Univ.. Dept. of Electrical and Electronics Engineering, Coimbatore (India)

    2012-07-01

    This work explores Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to forecast the average hourly wind speed. To determine the characteristics of ANFIS that best suited the target wind speed forecasting system, several ANFIS models were trained, tested and compared. Different types and number of inputs, training and checking sizes, type and number of membership functions and techniques to generate the initial (FIS) were analyzed. Comparisons with other forecasting methods were analyzed the models were given wind speed, direction and air pressure as inputs having the best forecasting accuracy. SCADA system is utilized to obtain the wind speed to the forecasting system in the host computer where ANFIS is present. The SCADA is located in the central room, the substation of the wind farm, or even at a remote off site point. The data obtained from the site is plotted at every instant and the predicted wind speed is displayed and also exported to the excel sheet which will be sent/e-mailed in the form of Graphs and excel sheets to the operator, State load dispatch centre (SLDC) and to the customer. (Author)

  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. Forecasting autoregressive time series under changing persistence

    DEFF Research Database (Denmark)

    Kruse, Robinson

    Changing persistence in time series models means that a structural change from nonstationarity to stationarity or vice versa occurs over time. Such a change has important implications for forecasting, as negligence may lead to inaccurate model predictions. This paper derives generally applicable...... recommendations, no matter whether a change in persistence occurs or not. Seven different forecasting strategies based on a biasedcorrected estimator are compared by means of a large-scale Monte Carlo study. The results for decreasing and increasing persistence are highly asymmetric and new to the literature. Its...

  14. Meteor Shower Forecasting for Spacecraft Operations

    Science.gov (United States)

    Moorhead, Althea V.; Cooke, William J.; Campbell-Brown, Margaret D.

    2017-01-01

    Although sporadic meteoroids are a much greater hazard to spacecraft than shower meteoroids in general, meteor showers can significantly increase the risk of damage over short time periods. Because showers are brief, it is sometimes possible to mitigate the risk operationally, which requires accurate predictions of shower activity. NASA's Meteoroid Environment Office generates an annual meteor shower forecast that describes the variations in the near-Earth meteoroid flux produced by meteor showers, which presents the shower flux both in absolute terms and relative to the sporadic ux. The shower forecast incorporates model predictions of annual variations in shower activity and quotes fluxes to several limiting particle kinetic energies. In this work, we describe our forecasting methods, compare them to actual observations, and highlight recent improvements to the temporal pro les based on flux measurements from the Canadian Meteor Orbit Radar (CMOR).

  15. Probing magma reservoirs to improve volcano forecasts

    Science.gov (United States)

    Lowenstern, Jacob B.; Sisson, Thomas W.; Hurwitz, Shaul

    2017-01-01

    When it comes to forecasting eruptions, volcano observatories rely mostly on real-time signals from earthquakes, ground deformation, and gas discharge, combined with probabilistic assessments based on past behavior [Sparks and Cashman, 2017]. There is comparatively less reliance on geophysical and petrological understanding of subsurface magma reservoirs.

  16. Forecasting Inflation through Econometrics Models: An Empirical ...

    African Journals Online (AJOL)

    This article aims at modeling and forecasting inflation in Pakistan. For this purpose a number of econometric approaches are implemented and their results are compared. In ARIMA models, adding additional lags for p and/or q necessarily reduced the sum of squares of the estimated residuals. When a model is estimated ...

  17. Ensemble methods for seasonal limited area forecasts

    DEFF Research Database (Denmark)

    Arritt, Raymond W.; Anderson, Christopher J.; Takle, Eugene S.

    2004-01-01

    The ensemble prediction methods used for seasonal limited area forecasts were examined by comparing methods for generating ensemble simulations of seasonal precipitation. The summer 1993 model over the north-central US was used as a test case. The four methods examined included the lagged....... The mixed-physics ensemble performed well in terms of equitable threat score, especially for higher precipitation amounts....

  18. Do experts' SKU forecasts improve after feedback?

    NARCIS (Netherlands)

    R. Legerstee (Rianne); Ph.H.B.F. Franses (Philip Hans)

    2011-01-01

    textabstractWe analyze the behavior of experts who quote forecasts for monthly SKU-level sales data where we compare data before and after the moment that experts received different kinds of feedback on their behavior. We have data for 21 experts located in as many countries who make

  19. Do Experts' SKU Forecasts improve after Feedback?

    NARCIS (Netherlands)

    R. Legerstee (Rianne); Ph.H.B.F. Franses (Philip Hans)

    2011-01-01

    textabstractWe analyze the behavior of experts who quote forecasts for monthly SKU-level sales data where we compare data before and after the moment that experts received different kinds of feedback on their behavior. We have data for 21 experts located in as many countries who make SKU-level

  20. Tsunami Forecasting in the Atlantic Basin

    Science.gov (United States)

    Knight, W. R.; Whitmore, P.; Sterling, K.; Hale, D. A.; Bahng, B.

    2012-12-01

    -computation - starting with those sources that carry the highest risk. Model computation zones are confined to regions at risk to save computation time. For example, Atlantic sources have been shown to not propagate into the Gulf of Mexico. Therefore, fine grid computations are not performed in the Gulf for Atlantic sources. Outputs from the Atlantic model include forecast marigrams at selected sites, maximum amplitudes, drawdowns, and currents for all coastal points. The maximum amplitude maps will be supplemented with contoured energy flux maps which show more clearly the effects of bathymetric features on tsunami wave propagation. During an event, forecast marigrams will be compared to observations to adjust the model results. The modified forecasts will then be used to set alert levels between coastal breakpoints, and provided to emergency management.

  1. 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 :07 to 2005:06. The authors then forecast one- to six-months-ahead house prices over the forecast horizon of 2005:07 to 2007:06. They then compare forecasts generated from the SBVAR's with those from an unrestricted Vector Autoregressive (VAR) and the Bayesian...

  2. Beating the random walk: a performance assessment of long-term interest rate forecasts

    NARCIS (Netherlands)

    den Butter, F.A.G.; Jansen, P.W.

    2013-01-01

    This article assesses the performance of a number of long-term interest rate forecast approaches, namely time series models, structural economic models, expert forecasts and combinations thereof. The predictive performance of these approaches is compared using outside sample forecast errors, where a

  3. A Systematic Literature Review and Network Meta-Analysis Comparing Once-Weekly Semaglutide with Other GLP-1 Receptor Agonists in Patients with Type 2 Diabetes Previously Receiving 1-2 Oral Anti-Diabetic Drugs.

    Science.gov (United States)

    Witkowski, Michal; Wilkinson, Lars; Webb, Neil; Weids, Alan; Glah, Divina; Vrazic, Hrvoje

    2018-04-19

    Once-weekly semaglutide is a new glucagon-like peptide-1 (GLP-1) analogue administered at a 1.0 or 0.5 mg dose. As head-to-head trials assessing once-weekly semaglutide as an add-on to 1-2 oral anti-diabetic drugs (OADs) vs other GLP-1 receptor agonists (GLP-1 RAs) are limited, a network meta-analysis (NMA) was performed. The objective was to assess the relative efficacy and safety of once-weekly semaglutide vs GLP-1 RAs in patients with type 2 diabetes (T2D) inadequately controlled on 1-2 OADs. A systematic literature review (SLR) was conducted in order to identify trials of GLP-1 RAs in patients inadequately controlled on 1-2 OADs. Data at 24 ± 4 weeks were extracted for efficacy and safety outcomes (feasible for analysis in a NMA), which included the key outcomes of change from baseline in glycated hemoglobin (HbA 1c ), systolic blood pressure (SBP), and weight, as well as discontinuation due to adverse events (AEs). Data were synthesized using a NMA and a Bayesian framework. In total, 26 studies were included across the base case analyses. Once-weekly semaglutide 1.0 mg was associated with significantly greater reductions in HbA 1c and weight vs all GLP-1 RA comparators. Once-weekly semaglutide 0.5 mg also achieved significantly greater reductions in HbA 1c and weight compared with the majority of other GLP-1 RAs. Both doses of once-weekly semaglutide were associated with similar odds of discontinuation due to AEs compared with other GLP-1 RAs. Overall, once-weekly semaglutide 1.0 mg as an add-on to 1-2 OADs is the most efficacious GLP-1 RA in terms of the reduction of HbA 1c and weight from baseline after 6 months of treatment. In addition, the analysis suggests that once-weekly semaglutide is well tolerated and not associated with an increase in discontinuations due to AEs compared with other GLP-1 RAs. Novo Nordisk.

  4. Load forecasting for supermarket refrigeration

    DEFF Research Database (Denmark)

    Bacher, Peder; Madsen, Henrik; Aalborg Nielsen, Henrik

    This report presents a study of models for forecasting the load for supermarket refrigeration. The data used for building the forecasting models consists of load measurements, local climate measurements and weather forecasts. The load measurements are from a supermarket located in a village...... in Denmark. The load for refrigeration is the sum of all cabinets in the supermarket, both low and medium temperature cabinets, and spans a period of one year. As input to the forecasting models the ambient temperature observed near the supermarket together with weather forecasts are used. Every hour...... the hourly load for refrigeration for the following 42 hours is forecasted. The forecast models are adaptive linear time-series models which are fitted with a computationally efficient recursive least squares scheme. The dynamic relations between the inputs and the load is modeled by simple transfer...

  5. A Comparison between Neural Networks and Traditional Forecasting Methods: A Case Study

    Directory of Open Access Journals (Sweden)

    C. A. Mitrea

    2009-10-01

    Full Text Available Forecasting accuracy drives the performance of inventory management. This study is to investigate and compare different forecasting methods like Moving Average (MA and Autoregressive Integrated Moving Average (ARIMA with Neural Networks (NN models as Feed-forward NN and Nonlinear Autoregressive network with eXogenous inputs (NARX. Data used to forecast is acquired from inventory database of Panasonic Refrigeration Devices Company located in Singapore. Results have shown that forecasting with NN offers better performance in comparison with traditional methods.

  6. Online forecasting of electrical load for distributed management of plug-in electric vehicles

    OpenAIRE

    Basu , Kaustav; Ovalle , Andres; Guo , Baoling; Hably , Ahmad; Bacha , Seddik; Hajar , Khaled

    2016-01-01

    International audience; The paper aims at making online forecast of electrical load at the MV-LV transformer level. Optimal management of the Plug-in Electric Vehicles (PEV) charging requires the forecast of the electrical load for future hours. The forecasting module needs to be online (i.e update and make forecast for the future hours, every hour). The inputs to the predictor are historical electrical and weather data. Various data driven machine learning algorithms are compared to derive t...

  7. Using soil moisture forecasts for sub-seasonal summer temperature predictions in Europe

    Science.gov (United States)

    Orth, René; Seneviratne, Sonia I.

    2014-12-01

    Soil moisture exhibits outstanding memory characteristics and plays a key role within the climate system. Especially through its impacts on the evapotranspiration of soils and plants, it may influence the land energy balance and therefore surface temperature. These attributes make soil moisture an important variable in the context of weather and climate forecasting. In this study we investigate the value of (initial) soil moisture information for sub-seasonal temperature forecasts. For this purpose we employ a simple water balance model to infer soil moisture from streamflow observations in 400 catchments across Europe. Running this model with forecasted atmospheric forcing, we derive soil moisture forecasts, which we then translate into temperature forecasts using simple linear relationships. The resulting temperature forecasts show skill beyond climatology up to 2 weeks in most of the considered catchments. Even if forecasting skills are rather small at longer lead times with significant skill only in some catchments at lead times of 3 and 4 weeks, this soil moisture-based approach shows local improvements compared to the monthly European Centre for Medium Range Weather Forecasting (ECMWF) temperature forecasts at these lead times. For both products (soil moisture-only forecast and ECMWF forecast), we find comparable or better forecast performance in the case of extreme events, especially at long lead times. Even though a product based on soil moisture information alone is not of practical relevance, our results indicate that soil moisture (memory) is a potentially valuable contributor to temperature forecast skill. Investigating the underlying soil moisture of the ECMWF forecasts we find good agreement with the simple model forecasts, especially at longer lead times. Analyzing the drivers of the temperature forecast skills we find that they are mainly controlled by the strengths of (1) the soil moisture-temperature coupling and (2) the soil moisture memory. We

  8. Peak Wind Tool for General Forecasting

    Science.gov (United States)

    Barrett, Joe H., III

    2010-01-01

    The expected peak wind speed of the day is an important forecast element in the 45th Weather Squadron's (45 WS) daily 24-Hour and Weekly Planning Forecasts. The forecasts are used for ground and space launch operations at the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The 45 WS also issues wind advisories for KSC/CCAFS when they expect wind gusts to meet or exceed 25 kt, 35 kt and 50 kt thresholds at any level from the surface to 300 ft. The 45 WS forecasters have indicated peak wind speeds are challenging to forecast, particularly in the cool season months of October - April. In Phase I of this task, the Applied Meteorology Unit (AMU) developed a tool to help the 45 WS forecast non-convective winds at KSC/CCAFS for the 24-hour period of 0800 to 0800 local time. The tool was delivered as a Microsoft Excel graphical user interface (GUI). The GUI displayed the forecast of peak wind speed, 5-minute average wind speed at the time of the peak wind, timing of the peak wind and probability the peak speed would meet or exceed 25 kt, 35 kt and 50 kt. For the current task (Phase II ), the 45 WS requested additional observations be used for the creation of the forecast equations by expanding the period of record (POR). Additional parameters were evaluated as predictors, including wind speeds between 500 ft and 3000 ft, static stability classification, Bulk Richardson Number, mixing depth, vertical wind shear, temperature inversion strength and depth and wind direction. Using a verification data set, the AMU compared the performance of the Phase I and II prediction methods. Just as in Phase I, the tool was delivered as a Microsoft Excel GUI. The 45 WS requested the tool also be available in the Meteorological Interactive Data Display System (MIDDS). The AMU first expanded the POR by two years by adding tower observations, surface observations and CCAFS (XMR) soundings for the cool season months of March 2007 to April 2009. The POR was expanded

  9. Z-M in lightning forecasting

    OpenAIRE

    Machina, Alexia J.

    2009-01-01

    Approved for public release, distribution unlimited Frozen hydrometeors are required for a storm to produce lightning. Previous research has made strong correlations between ice mass and lightning flash rate and lightning flash density. This study attempted to correlate ice mass to lightning potential Operational interest is centered at Cape Canaveral Air Force Station/Kennedy Space Center where accurate weather forecasting is vital to mission requirements, resource protection, and pe...

  10. Forecasting Epidemics Through Nonparametric Estimation of Time-Dependent Transmission Rates Using the SEIR Model.

    Science.gov (United States)

    Smirnova, Alexandra; deCamp, Linda; Chowell, Gerardo

    2017-05-02

    Deterministic and stochastic methods relying on early case incidence data for forecasting epidemic outbreaks have received increasing attention during the last few years. In mathematical terms, epidemic forecasting is an ill-posed problem due to instability of parameter identification and limited available data. While previous studies have largely estimated the time-dependent transmission rate by assuming specific functional forms (e.g., exponential decay) that depend on a few parameters, here we introduce a novel approach for the reconstruction of nonparametric time-dependent transmission rates by projecting onto a finite subspace spanned by Legendre polynomials. This approach enables us to effectively forecast future incidence cases, the clear advantage over recovering the transmission rate at finitely many grid points within the interval where the data are currently available. In our approach, we compare three regularization algorithms: variational (Tikhonov's) regularization, truncated singular value decomposition (TSVD), and modified TSVD in order to determine the stabilizing strategy that is most effective in terms of reliability of forecasting from limited data. We illustrate our methodology using simulated data as well as case incidence data for various epidemics including the 1918 influenza pandemic in San Francisco and the 2014-2015 Ebola epidemic in West Africa.

  11. Methodical bases of geodemographic forecasting

    Directory of Open Access Journals (Sweden)

    Катерина Сегіда

    2016-10-01

    Full Text Available The article deals with methodological features of the forecast of population size and composition. The essence and features of probabilistic demographic forecasting, methods, a component and dynamic ranks are considered; requirements to initial indicators for each type of the forecast are provided. It is noted that geo-demographic forecast is an important component of regional geo-demographic characteristic. Features of the demographic forecast development by component method (recursors of age are given, basic formulae of calculation, including the equation of demographic balance, a formula recursors taking into account gender and age indicators, survival coefficient are presented. The basic methodical principles of the demographic forecast are given by an extrapolation method (dynamic ranks, calculation features by means of the generalized indicators, such as extrapolation on the basis of indicators of an average pure gain, average growth rate and average rate of a gain are presented. To develop population forecast, the method of retrospective extrapolation (for the short-term forecast and a component method (for the mid-term forecast are mostly used. The example of such development by component method for gender and age structure of the population of Kharkiv region with step-by-step explanation of calculation is provided. The example of Kharkiv region’s population forecast development is provided by the method of dynamic ranks. Having carried out calculations of the main forecast indicators by administrative units, it is possible to determine features of further regional demographic development, to reveal internal territorial distinctions in demographic development. Application of separate forecasting methods allows to develop the forecast for certain indicators, however essential a variety, nonlinearity and not stationarity of the processes constituting demographic development forces to look +for new approaches and

  12. Forecasting Turbine Icing Events

    DEFF Research Database (Denmark)

    Davis, Neil; Hahmann, Andrea N.; Clausen, Niels-Erik

    2012-01-01

    In this study, we present a method for forecasting icing events. The method is validated at two European wind farms in with known icing events. The icing model used was developed using current ice accretion methods, and newly developed ablation algorithms. The model is driven by inputs from the WRF...... mesoscale model, allowing for both climatological estimates of icing and short term icing forecasts. The current model was able to detect periods of icing reasonably well at the warmer site. However at the cold climate site, the model was not able to remove ice quickly enough leading to large ice...... accumulations, which have not been seen in observations. In addition to the model evaluation we were able to investigate the potential occurrence of ice induced power loss at two wind parks in Europe using observed data. We found that the potential loss during an icing event is large even when the turbine...

  13. Forecasting Infectious Disease Outbreaks

    Science.gov (United States)

    Shaman, J. L.

    2015-12-01

    Dynamic models of infectious disease systems abound and are used to study the epidemiological characteristics of disease outbreaks, the ecological mechanisms affecting transmission, and the suitability of various control and intervention strategies. The dynamics of disease transmission are non-linear and consequently difficult to forecast. Here, we describe combined model-inference frameworks developed for the prediction of infectious diseases. We show that accurate and reliable predictions of seasonal influenza outbreaks can be made using a mathematical model representing population-level influenza transmission dynamics that has been recursively optimized using ensemble data assimilation techniques and real-time estimates of influenza incidence. Operational real-time forecasts of influenza and other infectious diseases have been and are currently being generated.

  14. Forecasting carbon dioxide emissions.

    Science.gov (United States)

    Zhao, Xiaobing; Du, Ding

    2015-09-01

    This study extends the literature on forecasting carbon dioxide (CO2) emissions by applying the reduced-form econometrics approach of Schmalensee et al. (1998) to a more recent sample period, the post-1997 period. Using the post-1997 period is motivated by the observation that the strengthening pace of global climate policy may have been accelerated since 1997. Based on our parameter estimates, we project 25% reduction in CO2 emissions by 2050 according to an economic and population growth scenario that is more consistent with recent global trends. Our forecasts are conservative due to that we do not have sufficient data to fully take into account recent developments in the global economy. Copyright © 2015 Elsevier Ltd. All rights reserved.

  15. Ensemble forecasting with machine learning algorithms for ozone, nitrogen dioxide and PM10 on the Prev'Air platform

    Science.gov (United States)

    Debry, E.; Mallet, V.

    2014-07-01

    This paper presents the application of an ensemble forecasting approach to the Prev’Air operational platform. This platform aims at forecasting maps, on a daily basis, for ozone, nitrogen dioxide and particulate matter. It relies on several air quality models which differ by their physical parameterizations, their input data and numerical strategies, so that one model may perform better with respect to observations for a given pollutant, at a given time and location. We apply sequential aggregation methods to this ensemble of models, which has already proved good potential in previous research papers. Compared to these studies, the novelties of this paper are the variety of models, the real operational context, which requires robustness assessment, and the application to several pollutants. In this paper, we first introduce the ensemble forecasting methods and the operational platform Prev’Air along with its models. Then, the sequential aggregation performance and robustness are assessed using two different data sets. The results with the discounted ridge regression method show that the errors of the forecasts are respectively reduced by at least 29%, 35% and 19% for hourly, daily and peak O3 concentrations, by 19%, 26% and 20% for hourly, daily and peak NO2 concentrations, and finally by 17%, 19% and 11% for hourly, daily and peak PM10 concentrations. At last, we give a first insight of the ensemble ability to forecast threshold exceedances.

  16. Wave Forecasting Using Neuro Wavelet Technique

    Directory of Open Access Journals (Sweden)

    Pradnya Dixit

    2014-12-01

    Full Text Available In the present work a hybrid Neuro-Wavelet Technique is used for forecasting waves up to 6 hr, 12 hr, 18 hr and 24 hr in advance using hourly measured significant wave heights at an NDBC station 41004 near the east coast of USA. The NW Technique is employed by combining two methods, Discrete Wavelet Transform and Artificial Neural Networks. The hourly data of previously measured significant wave heights spanning over 2 years from 2010 and 2011 is used to calibrate and test the models. The discrete wavelet transform of NWT analyzes frequency of signal with respect to time at different scales. It decomposes time series into low (approximate and high (detail frequency components. The decomposition of approximate can be carried out up to desired multiple levels in order to provide more detail and approximate components which provides relatively smooth varying amplitude series. The neural network is trained with decorrelated approximate and detail wavelet coefficients. The outputs of networks during testing are reconstructed back using inverse DWT. The results were judged by drawing the wave plots, scatter plots and other error measures. The developed models show reasonable accuracy in prediction of significant wave heights from 6 to 24 hours. To compare the results traditional ANN models were also developed at the same location using the same data and for same time interval.

  17. Forecasts of Agricultural Drought in Sri Lanka

    Science.gov (United States)

    Gilligan, J. M.; Gunda, T.; Hornberger, G. M.

    2015-12-01

    As the most frequent natural disaster in Sri Lanka, drought greatly affects crop production and livelihoods. Over half of all agricultural crop damage in Sri Lanka is currently due to drought; the frequency and severity of drought in the country is only expected to increase with the changing climate. Previous work indicates that the Palmer Drought Severity Index (PDSI) and Standardized Precipitation Index (SPI) are capable of capturing agricultural drought patterns (between 1881-2010) in the island nation. In this work, PDSI and SPI from 13 long-term meteorological stations will be projected into the future using a combination of artificial neural network and autoregressive integrated moving average models. The impacts of large-scale atmospheric circulation patterns (such as the Niño 3.4 index, a measure of sea surface temperature) and lead times on projection accuracy will also be explored. Model projections will be compared to weather data since 2010 to determine if the 2014 drought could have been forecasted using these methods. Since agricultural systems are strongly influenced by both natural and human systems, it is important to frame these physical findings within a social context. This work is part of an interdisciplinary project that assesses the perceptions of and adaptations to drought by rice farmers in Sri Lanka; disciplines represented in the group include hydrology, social psychology, ethnography, policy, and behavioral economics. Insights from the diverse research perspectives within the group will be drawn upon to highlight the social implications of the physical results.

  18. Ensemble approach to wheat yield forecasting in Ukraine

    Science.gov (United States)

    Kussul, Nataliia; Kolotii, Andrii; Skakun, Sergii; Shelestov, Andrii; Kussul, Olga; Kravchenko, Oleksii

    2014-05-01

    Crop yield forecasting is an extremely important component of the agriculture monitoring domain. In our previous study [1], we assessed relative efficiency and feasibility of using an NDVI-based empirical model for winter wheat yield forecasting at oblast level in Ukraine. Though the NDVI-based model provides minimum data requirements, it has some limitations since NDVI is indirectly related just to biomass but not meteorological conditions. Therefore, it is necessary to assess satellite-derived parameters that incorporate meteorology while maintaining the requirement of minimum data inputs. The objective of the proposed paper is several-fold: (i) to assess efficiency of using biophysical satellite-derived parameters for crop yield forecasting for Ukraine and select the optimal ones based on rigorous feature selection procedure; (ii) to assimilate predictions made by models built on various satellite-derived parameters. Two new parameters are considered in the paper: (i) vegetation health index (VHI) at 4 km spatial resolution derived from a series of NOAA satellites; (ii) Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) derived from SPOT-VEGETATION at 1 km spatial resolution. VHI data are provided as weekly composites and FAPAR data are provided as decadal composites. The particular advantage of using VHI is that it incorporates moisture and thermal conditions of vegetation canopy, while FAPAR is directly related to the primary productivity of photosynthesis It is required to find a day of the year for which a parameter is taken and used in the empirical model. For this purpose, a Random Forest feature selection procedure is applied. It is found that VHI and FAPAR values taken in April-May provided the minimum error value when comparing to the official statistics, thus enabling forecasts 2-3 months prior to harvest, and this corresponds to results derived from LOOCV procedure. The best timing for making reliable yield forecasts is nearly the same

  19. Statistical methods for forecasting

    CERN Document Server

    Abraham, Bovas

    2009-01-01

    The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists."This book, it must be said, lives up to the words on its advertising cover: ''Bridging the gap between introductory, descriptive approaches and highly advanced theoretical treatises, it provides a practical, intermediate level discussion of a variety of forecasting tools, and explains how they relate to one another, both in theory and practice.'' It does just that!"-Journal of the Royal Statistical Society"A well-written work that deals with statistical methods and models that can be used to produce short-term forecasts, this book has wide-ranging applications. It could be used in the context of a study of regression, forecasting, and time series ...

  20. An Assessment of the Subseasonal Forecast Performance in the Extended Global Ensemble Forecast System (GEFS)

    Science.gov (United States)

    Sinsky, E.; Zhu, Y.; Li, W.; Guan, H.; Melhauser, C.

    2017-12-01

    Optimal forecast quality is crucial for the preservation of life and property. Improving monthly forecast performance over both the tropics and extra-tropics requires attention to various physical aspects such as the representation of the underlying SST, model physics and the representation of the model physics uncertainty for an ensemble forecast system. This work focuses on the impact of stochastic physics, SST and the convection scheme on forecast performance for the sub-seasonal scale over the tropics and extra-tropics with emphasis on the Madden-Julian Oscillation (MJO). A 2-year period is evaluated using the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS). Three experiments with different configurations than the operational GEFS were performed to illustrate the impact of the stochastic physics, SST and convection scheme. These experiments are compared against a control experiment (CTL) which consists of the operational GEFS but its integration is extended from 16 to 35 days. The three configurations are: 1) SPs, which uses a Stochastically Perturbed Physics Tendencies (SPPT), Stochastic Perturbed Humidity (SHUM) and Stochastic Kinetic Energy Backscatter (SKEB); 2) SPs+SST_bc, which uses a combination of SPs and a bias-corrected forecast SST from the NCEP Climate Forecast System Version 2 (CFSv2); and 3) SPs+SST_bc+SA_CV, which combines SPs, a bias-corrected forecast SST and a scale aware convection scheme. When comparing to the CTL experiment, SPs shows substantial improvement. The MJO skill has improved by about 4 lead days during the 2-year period. Improvement is also seen over the extra-tropics due to the updated stochastic physics, where there is a 3.1% and a 4.2% improvement during weeks 3 and 4 over the northern hemisphere and southern hemisphere, respectively. Improvement is also seen when the bias-corrected CFSv2 SST is combined with SPs. Additionally, forecast performance enhances when the scale aware

  1. Parametric decadal climate forecast recalibration (DeFoReSt 1.0)

    Science.gov (United States)

    Pasternack, Alexander; Bhend, Jonas; Liniger, Mark A.; Rust, Henning W.; Müller, Wolfgang A.; Ulbrich, Uwe

    2018-01-01

    Near-term climate predictions such as decadal climate forecasts are increasingly being used to guide adaptation measures. For near-term probabilistic predictions to be useful, systematic errors of the forecasting systems have to be corrected. While methods for the calibration of probabilistic forecasts are readily available, these have to be adapted to the specifics of decadal climate forecasts including the long time horizon of decadal climate forecasts, lead-time-dependent systematic errors (drift) and the errors in the representation of long-term changes and variability. These features are compounded by small ensemble sizes to describe forecast uncertainty and a relatively short period for which typically pairs of reforecasts and observations are available to estimate calibration parameters. We introduce the Decadal Climate Forecast Recalibration Strategy (DeFoReSt), a parametric approach to recalibrate decadal ensemble forecasts that takes the above specifics into account. DeFoReSt optimizes forecast quality as measured by the continuous ranked probability score (CRPS). Using a toy model to generate synthetic forecast observation pairs, we demonstrate the positive effect on forecast quality in situations with pronounced and limited predictability. Finally, we apply DeFoReSt to decadal surface temperature forecasts from the MiKlip prototype system and find consistent, and sometimes considerable, improvements in forecast quality compared with a simple calibration of the lead-time-dependent systematic errors.

  2. Deep Neural Network Based Demand Side Short Term Load Forecasting

    Directory of Open Access Journals (Sweden)

    Seunghyoung Ryu

    2016-12-01

    Full Text Available In the smart grid, one of the most important research areas is load forecasting; it spans from traditional time series analyses to recent machine learning approaches and mostly focuses on forecasting aggregated electricity consumption. However, the importance of demand side energy management, including individual load forecasting, is becoming critical. In this paper, we propose deep neural network (DNN-based load forecasting models and apply them to a demand side empirical load database. DNNs are trained in two different ways: a pre-training restricted Boltzmann machine and using the rectified linear unit without pre-training. DNN forecasting models are trained by individual customer’s electricity consumption data and regional meteorological elements. To verify the performance of DNNs, forecasting results are compared with a shallow neural network (SNN, a double seasonal Holt–Winters (DSHW model and the autoregressive integrated moving average (ARIMA. The mean absolute percentage error (MAPE and relative root mean square error (RRMSE are used for verification. Our results show that DNNs exhibit accurate and robust predictions compared to other forecasting models, e.g., MAPE and RRMSE are reduced by up to 17% and 22% compared to SNN and 9% and 29% compared to DSHW.

  3. Forecasting natural gas consumption in China by Bayesian Model Averaging

    Directory of Open Access Journals (Sweden)

    Wei Zhang

    2015-11-01

    Full Text Available With rapid growth of natural gas consumption in China, it is in urgent need of more accurate and reliable models to make a reasonable forecast. Considering the limitations of the single model and the model uncertainty, this paper presents a combinative method to forecast natural gas consumption by Bayesian Model Averaging (BMA. It can effectively handle the uncertainty associated with model structure and parameters, and thus improves the forecasting accuracy. This paper chooses six variables for forecasting the natural gas consumption, including GDP, urban population, energy consumption structure, industrial structure, energy efficiency and exports of goods and services. The results show that comparing to Gray prediction model, Linear regression model and Artificial neural networks, the BMA method provides a flexible tool to forecast natural gas consumption that will have a rapid growth in the future. This study can provide insightful information on natural gas consumption in the future.

  4. Data Driven Broiler Weight Forecasting using Dynamic Neural Network Models

    DEFF Research Database (Denmark)

    Johansen, Simon Vestergaard; Bendtsen, Jan Dimon; Riisgaard-Jensen, Martin

    2017-01-01

    In this article, the dynamic influence of environmental broiler house conditions and broiler growth is investigated. Dynamic neural network forecasting models have been trained on farm-scale broiler batch production data from 12 batches from the same house. The model forecasts future broiler weight...... and uses environmental conditions such as heating, ventilation, and temperature along with broiler behavior such as feed and water consumption. Training data and forecasting data is analyzed to explain when the model might fail at generalizing. We present ensemble broiler weight forecasts to day 7, 14, 21......, 28 and 34 from all preceding days and provide our interpretation of the results. Results indicate that the dynamic interconnection between environmental conditions and broiler growth can be captured by the model. Furthermore, we found that a comparable forecast can be obtained by using input data...

  5. Operational Streamflow Forecasts Development Using GCM Predicted Precipitation Fields

    Science.gov (United States)

    Arumugam, S.; Lall, U.

    2004-12-01

    Monthly updates of streamflow forecasts are required for deriving reservoir operation strategies as well as for quantifying surplus and shortfall for the allocated water contracts. In this study, an operational streamflow forecasts are developed using Atmospheric General Circulation Models (AGCM) predicted precipitation for managing the Angat Reservoir System, Philippines. The methodology employs principal components regression (PCR) for downscaling the AGCM predicted precipitation fields to monthly streamflow forecasts. The performance of this downscaling approach is analyzed with AGCM being forced using the observed sea surface temperature (SST) conditions as well under persisted SST conditions. The ability of downscaled streamflow forecasts in explaining the intraseasonal variability is also explored. Conditional distribution of streamflows obtained from the PCR downscaling approach is also compared with a simple, semi-parametric resampling algorithm that obtains ensembles of streamflow forecasts by identifying similar conditions in that season's climatic predictors state space.

  6. Probabilistic forecasting of wind power generation using extreme learning machine

    DEFF Research Database (Denmark)

    Wan, Can; Xu, Zhao; Pinson, Pierre

    2014-01-01

    Accurate and reliable forecast of wind power is essential to power system operation and control. However, due to the nonstationarity of wind power series, traditional point forecasting can hardly be accurate, leading to increased uncertainties and risks for system operation. This paper proposes...... an extreme learning machine (ELM)-based probabilistic forecasting method for wind power generation. To account for the uncertainties in the forecasting results, several bootstrapmethods have been compared for modeling the regression uncertainty, based on which the pairs bootstrap method is identified...... with the best performance. Consequently, a new method for prediction intervals formulation based on theELMand the pairs bootstrap is developed.Wind power forecasting has been conducted in different seasons using the proposed approach with the historical wind power time series as the inputs alone. The results...

  7. A trend fixed on firstly and seasonal adjustment model combined with the ε-SVR for short-term forecasting of electricity demand

    International Nuclear Information System (INIS)

    Wang Jianzhou; Zhu Wenjin; Zhang Wenyu; Sun Donghuai

    2009-01-01

    Short-term electricity demand forecasting has always been an essential instrument in power system planning and operation by which an electric utility plans and dispatches loading so as to meet system demand. The accuracy of the dispatching system, derived from the accuracy of demand forecasting and the forecasting algorithm used, will determines the economic of the power system operation as well as the stability of the whole society. This paper presents a combined ε-SVR model considering seasonal proportions based on development tendencies from history data. We use one-order moving averages to produce a comparatively smooth data series, taking the averaging period as the interval that can effectively eliminate the seasonal variation. We used the smoothed data series as the training set input for the ε-SVR model and obtained the corresponding forecasting value. Afterward, we accounted for the previously removed seasonal variation. As a case, we forecast northeast electricity demand of China using the new method. We demonstrated that this simple procedure has very satisfactory overall performance by an analysis of variance with relative verification and validation. Significant reductions in forecast errors were achieved.

  8. A trend fixed on firstly and seasonal adjustment model combined with the epsilon-SVR for short-term forecasting of electricity demand

    Energy Technology Data Exchange (ETDEWEB)

    Wang Jianzhou [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China); Zhu Wenjin, E-mail: crying.1@hotmail.co [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China); Zhang Wenyu [College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000 (China); Sun Donghuai [Key Laboratory of Western Chinas Environmental Systems (Ministry of Education) College of Earth and Environment Sciences, Lanzhou University, Lanzhou 730000 (China)

    2009-11-15

    Short-term electricity demand forecasting has always been an essential instrument in power system planning and operation by which an electric utility plans and dispatches loading so as to meet system demand. The accuracy of the dispatching system, derived from the accuracy of demand forecasting and the forecasting algorithm used, will determines the economic of the power system operation as well as the stability of the whole society. This paper presents a combined epsilon-SVR model considering seasonal proportions based on development tendencies from history data. We use one-order moving averages to produce a comparatively smooth data series, taking the averaging period as the interval that can effectively eliminate the seasonal variation. We used the smoothed data series as the training set input for the epsilon-SVR model and obtained the corresponding forecasting value. Afterward, we accounted for the previously removed seasonal variation. As a case, we forecast northeast electricity demand of China using the new method. We demonstrated that this simple procedure has very satisfactory overall performance by an analysis of variance with relative verification and validation. Significant reductions in forecast errors were achieved.

  9. A trend fixed on firstly and seasonal adjustment model combined with the {epsilon}-SVR for short-term forecasting of electricity demand

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Jianzhou; Zhu, Wenjin [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China); Zhang, Wenyu [College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000 (China); Sun, Donghuai [Key Laboratory of Western Chinas Environmental Systems (Ministry of Education) College of Earth and Environment Sciences, Lanzhou University, Lanzhou 730000 (China)

    2009-11-15

    Short-term electricity demand forecasting has always been an essential instrument in power system planning and operation by which an electric utility plans and dispatches loading so as to meet system demand. The accuracy of the dispatching system, derived from the accuracy of demand forecasting and the forecasting algorithm used, will determines the economic of the power system operation as well as the stability of the whole society. This paper presents a combined {epsilon}-SVR model considering seasonal proportions based on development tendencies from history data. We use one-order moving averages to produce a comparatively smooth data series, taking the averaging period as the interval that can effectively eliminate the seasonal variation. We used the smoothed data series as the training set input for the {epsilon}-SVR model and obtained the corresponding forecasting value. Afterward, we accounted for the previously removed seasonal variation. As a case, we forecast northeast electricity demand of China using the new method. We demonstrated that this simple procedure has very satisfactory overall performance by an analysis of variance with relative verification and validation. Significant reductions in forecast errors were achieved. (author)

  10. Seasonal Verification of Dust Forecast over the Indian Region

    Science.gov (United States)

    Fatima, Hashmi; George, John P.; Rajagopal, E. N.; Basu, Swati

    2017-11-01

    The medium-range forecast of the dust aerosols over Indian region produced by the NCMRWF numerical weather prediction model with mineral dust scheme from May 2013 to May 2014 is examined in this study. Coarse mode aerosol observations are only used for comparison with dust forecast with the assumption that coarse mode aerosol over Indian region largely represents dust aerosol, especially over the areas of high dust load. Accuracy and trends of the day-to-day dust forecast are studied at three AERONET locations in Indo-Gangetic Plains (IGP) using surface and MODIS satellite retrievals of coarse mode aerosol optical depth for entire one year (May 2013-May 2014). Seasonal mean geographical distribution of the medium-range forecast of dust by the model over Indian region is validated with different satellite retrievals for all four seasons. Availability of suitable observations is one of the limiting factors and big challenges for the validation of the dust forecast. The main focus of this study is to assess dust forecast by the model over Indian region for all seasons, to know the biases and errors of the model forecast for its optimal use. The study finds that model dust forecast is comparable to AERONET observations over three locations for all seasons except monsoon season.

  11. Interest Rate Forecasting with Neural Networks

    OpenAIRE

    Jan Täppinen

    1998-01-01

    This paper compares neural networks and linear regression models in interest rate forecasting using US term structure data. The expectations hypothesis gets some extra support from the neural network model as compared to the regression model. A neural network with the whole yield curve spectre from the difference between 1 and 3-month rates to the difference between 5 and 10-year rates predicts changes in interest rates quite well. However, during 1994?1995 the neural networks (as well as the...

  12. Ensemble hydromoeteorological forecasting in Denmark

    DEFF Research Database (Denmark)

    Lucatero Villasenor, Diana

    of the main sources of uncertainty in hydrological forecasts. This is the reason why substantiated efforts to include information from Numerical Weather Predictors (NWP) or General Circulation Models (GCM) have been made over the last couple of decades. The present thesis expects to advance the field...... forecasts only about 15% and ET0 being the lowest at 15% for some months. The lowest skill of ET0 can be attributable to the combination of both T and incoming shortwave radiation (ISWR) bias from the GCM in addition to the added uncertainty for the model of the ET0 chosen (Makkink formula). Attempts...... steps. First, GCM-based streamflow forecasts exhibit biases that increase with lead time and, although these forecasts are sharper than the ESP forecasts, these biases lead to lower accuracy relative to ESP forecasts, especially at lead times larger than two months. Corrected GCM-based streamflow...

  13. Evaluation of Probabilistic Disease Forecasts.

    Science.gov (United States)

    Hughes, Gareth; Burnett, Fiona J

    2017-10-01

    The statistical evaluation of probabilistic disease forecasts often involves calculation of metrics defined conditionally on disease status, such as sensitivity and specificity. However, for the purpose of disease management decision making, metrics defined conditionally on the result of the forecast-predictive values-are also important, although less frequently reported. In this context, the application of scoring rules in the evaluation of probabilistic disease forecasts is discussed. An index of separation with application in the evaluation of probabilistic disease forecasts, described in the clinical literature, is also considered and its relation to scoring rules illustrated. Scoring rules provide a principled basis for the evaluation of probabilistic forecasts used in plant disease management. In particular, the decomposition of scoring rules into interpretable components is an advantageous feature of their application in the evaluation of disease forecasts.

  14. Subseasonal Predictability of Boreal Summer Monsoon Rainfall from Ensemble Forecasts

    Directory of Open Access Journals (Sweden)

    Nicolas Vigaud

    2017-10-01

    Full Text Available Subseasonal forecast skill over the broadly defined North American (NAM, West African (WAM and Asian (AM summer monsoon regions is investigated using three Ensemble Prediction Systems (EPS at sub-monthly lead times. Extended Logistic Regression (ELR is used to produce probabilistic forecasts of weekly and week 3–4 averages of precipitation with starts in May–Aug, over the 1999–2010 period. The ELR tercile category probabilities for each model gridpoint are then averaged together with equal weight. The resulting Multi-Model Ensemble (MME forecasts exhibit good reliability, but have generally low sharpness for forecasts beyond 1 week; Multi-model ensembling largely removes negative values of the Ranked Probability Skill Score (RPSS seen in individual forecasts, and broadly improves the skill obtained in any of the three individual models except for the AM. The MME week 3–4 forecasts have generally higher RPSS and comparable reliability over all monsoon regions, compared to week 3 or week 4 forecast separately. Skill is higher during La Niña compared to El Niño and ENSO-neutral conditions over the 1999–2010 period, especially for the NAM. Regionally averaged RPSS is significantly correlated with the Maden-Julian Oscillation (MJO for the AM and WAM. Our results indicate potential for skillful predictions at subseasonal time-scales over the three summer monsoon regions of the Northern Hemisphere.

  15. Short-term forecasting model for aggregated regional hydropower generation

    International Nuclear Information System (INIS)

    Monteiro, Claudio; Ramirez-Rosado, Ignacio J.; Fernandez-Jimenez, L. Alfredo

    2014-01-01

    Highlights: • Original short-term forecasting model for the hourly hydropower generation. • The use of NWP forecasts allows horizons of several days. • New variable to represent the capacity level for generating hydroelectric energy. • The proposed model significantly outperforms the persistence model. - Abstract: This paper presents an original short-term forecasting model of the hourly electric power production for aggregated regional hydropower generation. The inputs of the model are previously recorded values of the aggregated hourly production of hydropower plants and hourly water precipitation forecasts using Numerical Weather Prediction tools, as well as other hourly data (load demand and wind generation). This model is composed of three modules: the first one gives the prediction of the “monthly” hourly power production of the hydropower plants; the second module gives the prediction of hourly power deviation values, which are added to that obtained by the first module to achieve the final forecast of the hourly hydropower generation; the third module allows a periodic adjustment of the prediction of the first module to improve its BIAS error. The model has been applied successfully to the real-life case study of the short-term forecasting of the aggregated hydropower generation in Spain and Portugal (Iberian Peninsula Power System), achieving satisfactory results for the next-day forecasts. The model can be valuable for agents involved in electricity markets and useful for power system operations

  16. Assessing the Performance of MODIS NDVI and EVI for Seasonal Crop Yield Forecasting at the Ecodistrict Scale

    Directory of Open Access Journals (Sweden)

    Louis Kouadio

    2014-10-01

    Full Text Available Crop yield forecasting plays a vital role in coping with the challenges of the impacts of climate change on agriculture. Improvements in the timeliness and accuracy of yield forecasting by incorporating near real-time remote sensing data and the use of sophisticated statistical methods can improve our capacity to respond effectively to these challenges. The objectives of this study were (i to investigate the use of derived vegetation indices for the yield forecasting of spring wheat (Triticum aestivum L. from the Moderate resolution Imaging Spectroradiometer (MODIS at the ecodistrict scale across Western Canada with the Integrated Canadian Crop Yield Forecaster (ICCYF; and (ii to compare the ICCYF-model based forecasts and their accuracy across two spatial scales-the ecodistrict and Census Agricultural Region (CAR, namely in CAR with previously reported ICCYF weak performance. Ecodistricts are areas with distinct climate, soil, landscape and ecological aspects, whereas CARs are census-based/statistically-delineated areas. Agroclimate variables combined respectively with MODIS-NDVI and MODIS-EVI indices were used as inputs for the in-season yield forecasting of spring wheat during the 2000–2010 period. Regression models were built based on a procedure of a leave-one-year-out. The results showed that both agroclimate + MODIS-NDVI and agroclimate + MODIS-EVI performed equally well predicting spring wheat yield at the ECD scale. The mean absolute error percentages (MAPE of the models selected from both the two data sets ranged from 2% to 33% over the study period. The model efficiency index (MEI varied between −1.1 and 0.99 and −1.8 and 0.99, respectively for the agroclimate + MODIS-NDVI and agroclimate + MODIS-EVI data sets. Moreover, significant improvement in forecasting skill (with decreasing MAPE of 40% and 5 times increasing MEI, on average was obtained at the finer, ecodistrict spatial scale, compared to the coarser CAR scale. Forecast

  17. Forecasting experiments of a dynamical-statistical model of the sea surface temperature anomaly field based on the improved self-memorization principle

    Science.gov (United States)

    Hong, Mei; Chen, Xi; Zhang, Ren; Wang, Dong; Shen, Shuanghe; Singh, Vijay P.

    2018-04-01

    With the objective of tackling the problem of inaccurate long-term El Niño-Southern Oscillation (ENSO) forecasts, this paper develops a new dynamical-statistical forecast model of the sea surface temperature anomaly (SSTA) field. To avoid single initial prediction values, a self-memorization principle is introduced to improve the dynamical reconstruction model, thus making the model more appropriate for describing such chaotic systems as ENSO events. The improved dynamical-statistical model of the SSTA field is used to predict SSTA in the equatorial eastern Pacific and during El Niño and La Niña events. The long-term step-by-step forecast results and cross-validated retroactive hindcast results of time series T1 and T2 are found to be satisfactory, with a Pearson correlation coefficient of approximately 0.80 and a mean absolute percentage error (MAPE) of less than 15 %. The corresponding forecast SSTA field is accurate in that not only is the forecast shape similar to the actual field but also the contour lines are essentially the same. This model can also be used to forecast the ENSO index. The temporal correlation coefficient is 0.8062, and the MAPE value of 19.55 % is small. The difference between forecast results in spring and those in autumn is not high, indicating that the improved model can overcome the spring predictability barrier to some extent. Compared with six mature models published previously, the present model has an advantage in prediction precision and length, and is a novel exploration of the ENSO forecast method.

  18. Earthquake forecasting and its verification

    Directory of Open Access Journals (Sweden)

    J. R. Holliday

    2005-01-01

    Full Text Available No proven method is currently available for the reliable short time prediction of earthquakes (minutes to months. However, it is possible to make probabilistic hazard assessments for earthquake risk. In this paper we discuss a new approach to earthquake forecasting based on a pattern informatics (PI method which quantifies temporal variations in seismicity. The output, which is based on an association of small earthquakes with future large earthquakes, is a map of areas in a seismogenic region ('hotspots'' where earthquakes are forecast to occur in a future 10-year time span. This approach has been successfully applied to California, to Japan, and on a worldwide basis. Because a sharp decision threshold is used, these forecasts are binary--an earthquake is forecast either to occur or to not occur. The standard approach to the evaluation of a binary forecast is the use of the relative (or receiver operating characteristic (ROC diagram, which is a more restrictive test and less subject to bias than maximum likelihood tests. To test our PI method, we made two types of retrospective forecasts for California. The first is the PI method and the second is a relative intensity (RI forecast based on the hypothesis that future large earthquakes will occur where most smaller earthquakes have occurred in the recent past. While both retrospective forecasts are for the ten year period 1 January 2000 to 31 December 2009, we performed an interim analysis 5 years into the forecast. The PI method out performs the RI method under most circumstances.

  19. Local TEC Modelling and Forecasting using Neural Networks

    Science.gov (United States)

    Tebabal, A.; Radicella, S. M.; Nigussie, M.; Damtie, B.; Nava, B.; Yizengaw, E.

    2017-12-01

    Abstract Modelling the Earth's ionospheric characteristics is the focal task for the ionospheric community to mitigate its effect on the radio communication, satellite navigation and technologies. However, several aspects of modelling are still challenging, for example, the storm time characteristics. This paper presents modelling efforts of TEC taking into account solar and geomagnetic activity, time of the day and day of the year using neural networks (NNs) modelling technique. The NNs have been designed with GPS-TEC measured data from low and mid-latitude GPS stations. The training was conducted using the data obtained for the period from 2011 to 2014. The model prediction accuracy was evaluated using data of year 2015. The model results show that diurnal and seasonal trend of the GPS-TEC is well reproduced by the model for the two stations. The seasonal characteristics of GPS-TEC is compared with NN and NeQuick 2 models prediction when the latter one is driven by the monthly average value of solar flux. It is found that NN model performs better than the corresponding NeQuick 2 model for low latitude region. For the mid-latitude both NN and NeQuick 2 models reproduce the average characteristics of TEC variability quite successfully. An attempt of one day ahead forecast of TEC at the two locations has been made by introducing as driver previous day solar flux and geomagnetic index values. The results show that a reasonable day ahead forecast of local TEC can be achieved.

  20. Medium-range fire weather forecasts

    Science.gov (United States)

    J.O. Roads; K. Ueyoshi; S.C. Chen; J. Alpert; F. Fujioka

    1991-01-01

    The forecast skill of theNational Meteorological Center's medium range forecast (MRF) numerical forecasts of fire weather variables is assessed for the period June 1,1988 to May 31,1990. Near-surface virtual temperature, relative humidity, wind speed and a derived fire weather index (FWI) are forecast well by the MRF model. However, forecast relative humidity has...

  1. Enhanced Forecasting Approach for Electricity Market Prices and Wind Power Data Series in the Short-Term

    Directory of Open Access Journals (Sweden)

    Gerardo J. Osório

    2016-08-01

    Full Text Available The uncertainty and variability in electricity market price (EMP signals and players’ behavior, as well as in renewable power generation, especially wind power, pose considerable challenges. Hence, enhancement of forecasting approaches is required for all electricity market players to deal with the non-stationary and stochastic nature of such time series, making it possible to accurately support their decisions in a competitive environment with lower forecasting error and with an acceptable computational time. As previously published methodologies have shown, hybrid approaches are good candidates to overcome most of the previous concerns about time-series forecasting. In this sense, this paper proposes an enhanced hybrid approach composed of an innovative combination of wavelet transform (WT, differential evolutionary particle swarm optimization (DEEPSO, and an adaptive neuro-fuzzy inference system (ANFIS to forecast EMP signals in different electricity markets and wind power in Portugal, in the short-term, considering only historical data. Test results are provided by comparing with other reported studies, demonstrating the proficiency of the proposed hybrid approach in a real environment.

  2. Analysis and Forecast of a Tornadic Thunderstorm Using Multiple Doppler Radar Data, 3DVAR, and ARPS Model

    Directory of Open Access Journals (Sweden)

    Edward Natenberg

    2013-01-01

    Full Text Available A three-dimensional variational (3DVAR assimilation technique developed for a convective-scale NWP model—advanced regional prediction system (ARPS—is used to analyze the 8 May 2003, Moore/Midwest City, Oklahoma tornadic supercell thunderstorm. Previous studies on this case used only one or two radars that are very close to this storm. However, three other radars observed the upper-level part of the storm. Because these three radars are located far away from the targeted storm, they were overlooked by previous studies. High-frequency intermittent 3DVAR analyses are performed using the data from five radars that together provide a more complete picture of this storm. The analyses capture a well-defined mesocyclone in the midlevels and the wind circulation associated with a hook-shaped echo. The analyses produced through this technique are used as initial conditions for a 40-minute storm-scale forecast. The impact of multiple radars on a short-term NWP forecast is most evident when compared to forecasts using data from only one and two radars. The use of all radars provides the best forecast in which a strong low-level mesocyclone develops and tracks in close proximity to the actual tornado damage path.

  3. Forecasting freight flows

    DEFF Research Database (Denmark)

    Lyk-Jensen, Stéphanie

    2011-01-01

    model. The analysis uses a structural relationship to explain the structure of the exchange of the goods—a relationship that can be used in the year of forecast. This article also provides a new methodology for converting monetary aggregates into quantity aggregates. The resulting commodity growth rates....... This article models long-term dynamic physical trade flows and estimates a dynamic panel data model for foreign trade for the EU15 and two countries from the EFTA (European Free Trade Association) 1967–2002. The analysis suggests that a dynamic three-way-effects gravity equation is the best-fitted econometric...

  4. Utility usage forecasting

    Science.gov (United States)

    Hosking, Jonathan R. M.; Natarajan, Ramesh

    2017-08-22

    The computer creates a utility demand forecast model for weather parameters by receiving a plurality of utility parameter values, wherein each received utility parameter value corresponds to a weather parameter value. Determining that a range of weather parameter values lacks a sufficient amount of corresponding received utility parameter values. Determining one or more utility parameter values that corresponds to the range of weather parameter values. Creating a model which correlates the received and the determined utility parameter values with the corresponding weather parameters values.

  5. Wind power forecasting accuracy and uncertainty in Finland

    Energy Technology Data Exchange (ETDEWEB)

    Holttinen, H.; Miettinen, J.; Sillanpaeae, S.

    2013-04-15

    Wind power cannot be dispatched so the production levels need to be forecasted for electricity market trading. Lower prediction errors mean lower regulation balancing costs, since relatively less energy needs to go through balance settlement. From the power system operator point of view, wind power forecast errors will impact the system net imbalances when the share of wind power increases, and more accurate forecasts mean less regulating capacity will be activated from the real time Regulating Power Market. In this publication short term forecasting of wind power is studied mainly from a wind power producer point of view. The forecast errors and imbalance costs from the day-ahead Nordic electricity markets are calculated based on real data from distributed wind power plants. Improvements to forecasting accuracy are presented using several wind forecast providers, and measures for uncertainty of the forecast are presented. Aggregation of sites lowers relative share of prediction errors considerably, up to 60%. The balancing costs were also reduced up to 60%, from 3 euro/MWh for one site to 1-1.4 euro/MWh to aggregate 24 sites. Pooling wind power production for balance settlement will be very beneficial, and larger producers who can have sites from larger geographical area will benefit in lower imbalance costs. The aggregation benefits were already significant for smaller areas, resulting in 30-40% decrease in forecast errors and 13-36% decrease in unit balancing costs, depending on the year. The resulting costs are strongly dependent on Regulating Market prices that determine the prices for the imbalances. Similar level of forecast errors resulted in 40% higher imbalance costs for 2012 compared with 2011. Combining wind forecasts from different Numerical Weather Prediction providers was studied with different combination methods for 6 sites. Averaging different providers' forecasts will lower the forecast errors by 6% for day-ahead purposes. When combining

  6. Ensemble flare forecasting: using numerical weather prediction techniques to improve space weather operations

    Science.gov (United States)

    Murray, S.; Guerra, J. A.

    2017-12-01

    One essential component of operational space weather forecasting is the prediction of solar flares. Early flare forecasting work focused on statistical methods based on historical flaring rates, but more complex machine learning methods have been developed in recent years. A multitude of flare forecasting methods are now available, however it is still unclear which of these methods performs best, and none are substantially better than climatological forecasts. Current operational space weather centres cannot rely on automated methods, and generally use statistical forecasts with a little human intervention. Space weather researchers are increasingly looking towards methods used in terrestrial weather to improve current forecasting techniques. Ensemble forecasting has been used in numerical weather prediction for many years as a way to combine different predictions in order to obtain a more accurate result. It has proved useful in areas such as magnetospheric modelling and coronal mass ejection arrival analysis, however has not yet been implemented in operational flare forecasting. Here we construct ensemble forecasts for major solar flares by linearly combining the full-disk probabilistic forecasts from a group of operational forecasting methods (ASSA, ASAP, MAG4, MOSWOC, NOAA, and Solar Monitor). Forecasts from each method are weighted by a factor that accounts for the method's ability to predict previous events, and several performance metrics (both probabilistic and categorical) are considered. The results provide space weather forecasters with a set of parameters (combination weights, thresholds) that allow them to select the most appropriate values for constructing the 'best' ensemble forecast probability value, according to the performance metric of their choice. In this way different forecasts can be made to fit different end-user needs.

  7. On the Influence of Weather Forecast Errors in Short-Term Load Forecasting Models

    OpenAIRE

    Fay, D.; Ringwood, John; Condon, M.

    2004-01-01

    Weather information is an important factor in load forecasting models. This weather information usually takes the form of actual weather readings. However, online operation of load forecasting models requires the use of weather forecasts, with associated weather forecast errors. A technique is proposed to model weather forecast errors to reflect current accuracy. A load forecasting model is then proposed which combines the forecasts of several load forecasting models. This approach allows the...

  8. Weighting of NMME temperature and precipitation forecasts across Europe

    Science.gov (United States)

    Slater, Louise J.; Villarini, Gabriele; Bradley, A. Allen

    2017-09-01

    Multi-model ensemble forecasts are obtained by weighting multiple General Circulation Model (GCM) outputs to heighten forecast skill and reduce uncertainties. The North American Multi-Model Ensemble (NMME) project facilitates the development of such multi-model forecasting schemes by providing publicly-available hindcasts and forecasts online. Here, temperature and precipitation forecasts are enhanced by leveraging the strengths of eight NMME GCMs (CCSM3, CCSM4, CanCM3, CanCM4, CFSv2, GEOS5, GFDL2.1, and FLORb01) across all forecast months and lead times, for four broad climatic European regions: Temperate, Mediterranean, Humid-Continental and Subarctic-Polar. We compare five different approaches to multi-model weighting based on the equally weighted eight single-model ensembles (EW-8), Bayesian updating (BU) of the eight single-model ensembles (BU-8), BU of the 94 model members (BU-94), BU of the principal components of the eight single-model ensembles (BU-PCA-8) and BU of the principal components of the 94 model members (BU-PCA-94). We assess the forecasting skill of these five multi-models and evaluate their ability to predict some of the costliest historical droughts and floods in recent decades. Results indicate that the simplest approach based on EW-8 preserves model skill, but has considerable biases. The BU and BU-PCA approaches reduce the unconditional biases and negative skill in the forecasts considerably, but they can also sometimes diminish the positive skill in the original forecasts. The BU-PCA models tend to produce lower conditional biases than the BU models and have more homogeneous skill than the other multi-models, but with some loss of skill. The use of 94 NMME model members does not present significant benefits over the use of the 8 single model ensembles. These findings may provide valuable insights for the development of skillful, operational multi-model forecasting systems.

  9. The Use of Ensemble Forecast Systems in MJO Prediction

    Science.gov (United States)

    Gottschalck, J.; Wheeler, M.; Weickmann, K.; Waliser, D.; Sperber, K.; Collins, D.

    2009-05-01

    The U.S. Climate Variability and Predictability (CLIVAR) MJO working group (MJOWG) has outlined a strategy to develop and apply a uniform metric for real-time dynamical MJO forecasts. The purpose of this activity is to provide a means to (1) quantitatively compare MJO forecast skill across operational Centres, (2) measure gains in forecast skill over time by a given Centre and the community as a whole, (3) facilitate the development of a multi-model forecast of the MJO, and (4) judge proposed forecast model improvements relative to their performance in representing the MJO. This paper focuses on the use of ensemble forecast systems as part of this project in this new arena. The current status, initial results and future plans of the activity in the context of ensemble information is described. The agreed upon MJO metric is based on extensive deliberations amongst the MJOWG in conjunction with input from a number of operational Centres and follows closely that outlined by Wheeler and Hendon (2004). At the current time, nine operational forecast Centres are or have agreed to participate and include NCEP (US), ECMWF (UK), UKMET (UK), CMC (Canada), ABOM (Australia), CWB (Taiwan), IMD (India), JMA (Japan), and CPTEC (Brazil). The Climate Prediction Center (CPC) within NCEP is hosting the acquisition of the data, application of the MJO metric and realtime display of the standardized forecasts. The activity has already seen application in an operational setting and further standardizing of the forecasts and their illustration along with systematic verification is expected to increase the usefulness and application of these forecasts and lead to more skillful predictions of the MJO and indirectly extra-tropical weather variability influenced by the MJO.

  10. Forecasting the value of credit scoring

    Science.gov (United States)

    Saad, Shakila; Ahmad, Noryati; Jaffar, Maheran Mohd

    2017-08-01

    Nowadays, credit scoring system plays an important role in banking sector. This process is important in assessing the creditworthiness of customers requesting credit from banks or other financial institutions. Usually, the credit scoring is used when customers send the application for credit facilities. Based on the score from credit scoring, bank will be able to segregate the "good" clients from "bad" clients. However, in most cases the score is useful at that specific time only and cannot be used to forecast the credit worthiness of the same applicant after that. Hence, bank will not know if "good" clients will always be good all the time or "bad" clients may become "good" clients after certain time. To fill up the gap, this study proposes an equation to forecast the credit scoring of the potential borrowers at a certain time by using the historical score related to the assumption. The Mean Absolute Percentage Error (MAPE) is used to measure the accuracy of the forecast scoring. Result shows the forecast scoring is highly accurate as compared to actual credit scoring.

  11. Seasonal forecasting of discharge for the Raccoon River, Iowa

    Science.gov (United States)

    Slater, Louise; Villarini, Gabriele; Bradley, Allen; Vecchi, Gabriel

    2016-04-01

    The state of Iowa (central United States) is regularly afflicted by severe natural hazards such as the 2008/2013 floods and the 2012 drought. To improve preparedness for these catastrophic events and allow Iowans to make more informed decisions about the most suitable water management strategies, we have developed a framework for medium to long range probabilistic seasonal streamflow forecasting for the Raccoon River at Van Meter, a 8900-km2 catchment located in central-western Iowa. Our flow forecasts use statistical models to predict seasonal discharge for low to high flows, with lead forecasting times ranging from one to ten months. Historical measurements of daily discharge are obtained from the U.S. Geological Survey (USGS) at the Van Meter stream gage, and used to compute quantile time series from minimum to maximum seasonal flow. The model is forced with basin-averaged total seasonal precipitation records from the PRISM Climate Group and annual row crop production acreage from the U.S. Department of Agriculture's National Agricultural Statistics Services database. For the forecasts, we use corn and soybean production from the previous year (persistence forecast) as a proxy for the impacts of agricultural practices on streamflow. The monthly precipitation forecasts are provided by eight Global Climate Models (GCMs) from the North American Multi-Model Ensemble (NMME), with lead times ranging from 0.5 to 11.5 months, and a resolution of 1 decimal degree. Additionally, precipitation from the month preceding each season is used to characterize antecedent soil moisture conditions. The accuracy of our modelled (1927-2015) and forecasted (2001-2015) discharge values is assessed by comparison with the observed USGS data. We explore the sensitivity of forecast skill over the full range of lead times, flow quantiles, forecast seasons, and with each GCM. Forecast skill is also examined using different formulations of the statistical models, as well as NMME forecast

  12. Flood Forecasting Based on TIGGE Precipitation Ensemble Forecast

    Directory of Open Access Journals (Sweden)

    Jinyin Ye

    2016-01-01

    Full Text Available TIGGE (THORPEX International Grand Global Ensemble was a major part of the THORPEX (Observing System Research and Predictability Experiment. It integrates ensemble precipitation products from all the major forecast centers in the world and provides systematic evaluation on the multimodel ensemble prediction system. Development of meteorologic-hydrologic coupled flood forecasting model and early warning model based on the TIGGE precipitation ensemble forecast can provide flood probability forecast, extend the lead time of the flood forecast, and gain more time for decision-makers to make the right decision. In this study, precipitation ensemble forecast products from ECMWF, NCEP, and CMA are used to drive distributed hydrologic model TOPX. We focus on Yi River catchment and aim to build a flood forecast and early warning system. The results show that the meteorologic-hydrologic coupled model can satisfactorily predict the flow-process of four flood events. The predicted occurrence time of peak discharges is close to the observations. However, the magnitude of the peak discharges is significantly different due to various performances of the ensemble prediction systems. The coupled forecasting model can accurately predict occurrence of the peak time and the corresponding risk probability of peak discharge based on the probability distribution of peak time and flood warning, which can provide users a strong theoretical foundation and valuable information as a promising new approach.

  13. Interactive Forecasting with the National Weather Service River Forecast System

    Science.gov (United States)

    Smith, George F.; Page, Donna

    1993-01-01

    The National Weather Service River Forecast System (NWSRFS) consists of several major hydrometeorologic subcomponents to model the physics of the flow of water through the hydrologic cycle. The entire NWSRFS currently runs in both mainframe and minicomputer environments, using command oriented text input to control the system computations. As computationally powerful and graphically sophisticated scientific workstations became available, the National Weather Service (NWS) recognized that a graphically based, interactive environment would enhance the accuracy and timeliness of NWS river and flood forecasts. Consequently, the operational forecasting portion of the NWSRFS has been ported to run under a UNIX operating system, with X windows as the display environment on a system of networked scientific workstations. In addition, the NWSRFS Interactive Forecast Program was developed to provide a graphical user interface to allow the forecaster to control NWSRFS program flow and to make adjustments to forecasts as necessary. The potential market for water resources forecasting is immense and largely untapped. Any private company able to market the river forecasting technologies currently developed by the NWS Office of Hydrology could provide benefits to many information users and profit from providing these services.

  14. Payette River Basin Project: Improving Operational Forecasting in Complex Terrain through Chemistry

    Science.gov (United States)

    Blestrud, D.; Kunkel, M. L.; Parkinson, S.; Holbrook, V. P.; Benner, S. G.; Fisher, J.

    2015-12-01

    Idaho Power Company (IPC) is an investor owned hydroelectric based utility, serving customers throughout southern Idaho and eastern Oregon. The University of Arizona (UA) runs an operational 1.8-km resolution Weather and Research Forecast (WRF) model for IPC, which is incorporated into IPC near and real-time forecasts for hydro, solar and wind generation, load servicing and a large-scale wintertime cloud seeding operation to increase winter snowpack. Winter snowpack is critical to IPC, as hydropower provides ~50% of the company's generation needs. In efforts to improve IPC's near-term forecasts and operational guidance to its cloud seeding program, IPC is working extensively with UA and the National Center for Atmospheric Research (NCAR) to improve WRF performance in the complex terrain of central Idaho. As part of this project, NCAR has developed a WRF based cloud seeding module (WRF CS) to deliver high-resolution, tailored forecasts to provide accurate guidance for IPC's operations. Working with Boise State University (BSU), IPC is conducting a multiyear campaign to validate the WRF CS's ability to account for and disperse the cloud seeding agent (AgI) within the boundary layer. This improved understanding of how WRF handles the AgI dispersion and fate will improve the understanding and ultimately the performance of WRF to forecast other parameters. As part of this campaign, IPC has developed an extensive ground based monitoring network including a Remote Area Snow Sampling Device (RASSD) that provides spatially and temporally discrete snow samples during active cloud seeding periods. To quantify AgI dispersion in the complex terrain, BSU conducts trace element analysis using LA-ICP-MS on the RASSD sampled snow to provide measurements (at the 10-12 level) of incorporated AgI, measurements are compare directly with WRF CS's estimates of distributed AgI. Modeling and analysis results from previous year's research and plans for coming seasons will be presented.

  15. A quality assessment of the MARS crop yield forecasting system for the European Union

    Science.gov (United States)

    van der Velde, Marijn; Bareuth, Bettina

    2015-04-01

    Timely information on crop production forecasts can become of increasing importance as commodity markets are more and more interconnected. Impacts across large crop production areas due to (e.g.) extreme weather and pest outbreaks can create ripple effects that may affect food prices and availability elsewhere. The MARS Unit (Monitoring Agricultural ResourceS), DG Joint Research Centre, European Commission, has been providing forecasts of European crop production levels since 1993. The operational crop production forecasting is carried out with the MARS Crop Yield Forecasting System (M-CYFS). The M-CYFS is used to monitor crop growth development, evaluate short-term effects of anomalous meteorological events, and provide monthly forecasts of crop yield at national and European Union level. The crop production forecasts are published in the so-called MARS bulletins. Forecasting crop yield over large areas in the operational context requires quality benchmarks. Here we present an analysis of the accuracy and skill of past crop yield forecasts of the main crops (e.g. soft wheat, grain maize), throughout the growing season, and specifically for the final forecast before harvest. Two simple benchmarks to assess the skill of the forecasts were defined as comparing the forecasts to 1) a forecast equal to the average yield and 2) a forecast using a linear trend established through the crop yield time-series. These reveal a variability in performance as a function of crop and Member State. In terms of production, the yield forecasts of 67% of the EU-28 soft wheat production and 80% of the EU-28 maize production have been forecast superior to both benchmarks during the 1993-2013 period. In a changing and increasingly variable climate crop yield forecasts can become increasingly valuable - provided they are used wisely. We end our presentation by discussing research activities that could contribute to this goal.

  16. Day-ahead wind speed forecasting using f-ARIMA models

    International Nuclear Information System (INIS)

    Kavasseri, Rajesh G.; Seetharaman, Krithika

    2009-01-01

    With the integration of wind energy into electricity grids, it is becoming increasingly important to obtain accurate wind speed/power forecasts. Accurate wind speed forecasts are necessary to schedule dispatchable generation and tariffs in the day-ahead electricity market. This paper examines the use of fractional-ARIMA or f-ARIMA models to model, and forecast wind speeds on the day-ahead (24 h) and two-day-ahead (48 h) horizons. The models are applied to wind speed records obtained from four potential wind generation sites in North Dakota. The forecasted wind speeds are used in conjunction with the power curve of an operational (NEG MICON, 750 kW) turbine to obtain corresponding forecasts of wind power production. The forecast errors in wind speed/power are analyzed and compared with the persistence model. Results indicate that significant improvements in forecasting accuracy are obtained with the proposed models compared to the persistence method. (author)

  17. Modeling Philippine Stock Exchange Composite Index Using Weighted Geometric Brownian Motion Forecasts

    Directory of Open Access Journals (Sweden)

    Gayo Willy

    2016-01-01

    Full Text Available Philippine Stock Exchange Composite Index (PSEi is the main stock index of the Philippine Stock Exchange (PSE. PSEi is computed using a weighted mean of the top 30 publicly traded companies in the Philippines, called component stocks. It provides a single value by which the performance of the Philippine stock market is measured. Unfortunately, these weights, which may vary for every trading day, are not disclosed by the PSE. In this paper, we propose a model of forecasting the PSEi by estimating the weights based on historical data and forecasting each component stock using Monte Carlo simulation based on a Geometric Brownian Motion (GBM assumption. The model performance is evaluated and its forecast compared is with the results using a direct GBM forecast of PSEi over different forecast periods. Results showed that the forecasts using WGBM will yield smaller error compared to direct GBM forecast of PSEi.

  18. The Variance-covariance Method using IOWGA Operator for Tourism Forecast Combination

    Directory of Open Access Journals (Sweden)

    Liangping Wu

    2014-08-01

    Full Text Available Three combination methods commonly used in tourism forecasting are the simple average method, the variance-covariance method and the discounted MSFE method. These methods assign the different weights that can not change at each time point to each individual forecasting model. In this study, we introduce the IOWGA operator combination method which can overcome the defect of previous three combination methods into tourism forecasting. Moreover, we further investigate the performance of the four combination methods through the theoretical evaluation and the forecasting evaluation. The results of the theoretical evaluation show that the IOWGA operator combination method obtains extremely well performance and outperforms the other forecast combination methods. Furthermore, the IOWGA operator combination method can be of well forecast performance and performs almost the same to the variance-covariance combination method for the forecasting evaluation. The IOWGA operator combination method mainly reflects the maximization of improving forecasting accuracy and the variance-covariance combination method mainly reflects the decrease of the forecast error. For future research, it may be worthwhile introducing and examining other new combination methods that may improve forecasting accuracy or employing other techniques to control the time for updating the weights in combined forecasts.

  19. Spatio-temporal modelling for short term wind power forecasts. Why, when and how.

    Science.gov (United States)

    Lenzi, Amanda; Steinsland, Ingelin; Pinson, Pierre

    2017-04-01

    This study is based on a case study of 349 wind farms in Western Denmark with available energy production every 15 minutes for 6 years. Our aim is to do short term forecasting up to 5 hours ahead based on previous observations. We want sharp and calibrated probabilistic forecasts for both individual wind farms and for aggregated energy production, for example the energy production in the whole region. To obtain this we propose two Bayesian spatio-temporal models, and obtain full probabilistic forecasts of wind power. The models are based on the stochastic partial differential equation (SPDE) approach to spatial-temporal modelling which enables fast inference using integrated nested Laplace approximations (INLA) as well as dimension reduction. We provide detailed analysis on the forecast performances on the individual and aggregated level based on appropriate metrics tailored for probability forecasts for both the spatial temporal models as well as for temporal models for individual wind farms. The case study as well as simulation studies demonstrate that forecasts that are individually reliable do not need to produce an aggregated forecasts that are reliable. Indeed, the case study shows that even when all individual forecasts are calibrated can the aggregated forecasts be so uncalibrated that less that 20% of the observations fall within the 95% forecast interval. T he results and methodology are both relevant for wind power forecasts in other regions as well as for spatial-temporal modeling and decisions in general.

  20. 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...... competition with four tracks on load, price, wind and solar forecasting, which attracted 581 participants from 61 countries. We conclude the paper with 12 predictions for the next decade of energy forecasting.......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...

  1. Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting

    Directory of Open Access Journals (Sweden)

    Hong-Juan Li

    2013-04-01

    Full Text Available Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR, this paper presents a SVR model hybridized with the empirical mode decomposition (EMD method and auto regression (AR for electric load forecasting. The electric load data of the New South Wales (Australia market are employed for comparing the forecasting performances of different forecasting models. The results confirm the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.

  2. Issues in Forecasting CMEs

    Science.gov (United States)

    Pizzo, V. J.

    2017-12-01

    I will present my view of the current status of space weather forecasting abilities related to CMEs. This talk will address the large-scale aspects, but specifically not energetic particle phenomena. A key point is that all models, whether sophisticated numerical contraptions or quasi-empirical ones, are only as good as the data you feed them. Hence the emphasis will be on observations and analysis methods. First I will review where we stand with regard to the near-Sun quantitative data needed to drive any model, no matter how complex or simple-minded, and I will discuss technological roadblocks that suggest it may be some time before we see any meaningful improvements beyond what we have today. Then I cover issues related to characterizing CME propagation out through the corona and into interplanetary space, as well as to observational limitations in the vicinity of 1 AU. Since none of these observational constraints are likely to be resolved anytime soon, the real challenge is to make more informed use of what is available. Thus, this talk will focus on how we may identify and pursue the most profitable approaches, for both forecast and research applications. The discussion will highlight a number of promising leads, including those related to inclusion of solar backside information, joint magnetograph observations from L5 and Earth, how to use (not just run) ensembles, more rational use of HI observations, and suggestions for using cube-sats for deep space observations of CMEs and MCs.

  3. Global warming forecasts unreliability

    International Nuclear Information System (INIS)

    Baker, A.B.

    1993-01-01

    This paper reports the opinions of a series of experts who have recently commented on the reliability of predictions of global warning in relation to observed and forecasted increases in carbon dioxide emissions. One of the more difficult to explain observations, evidenced through the analysis of past meteorological data, was the rapid increase in global temperature that took place during the period preceding 1940 and which was followed by a gradual decrease, during a thirty year period of heightened industrialization and consumption of fossil fuels, up to 1970 when global temperatures began again to rise rapidly. Variations in solar activity was suggested to explain this apparently anomalous trend in global temperatures. This question as to the existence of a strict correlation between global warming and rises in carbon dioxide emissions, as well as, forecasted increases in concentrations of atmospheric carbon dioxide due to the expected population growth in China are putting a strain on attempts by OECD (Organization for Economic Co-operation and Development) environmental policy makers to gain support for energy tax proposals

  4. Wind Speed Forecasting Using Hybrid Wavelet Transform—ARMA Techniques

    Directory of Open Access Journals (Sweden)

    Diksha Kaur

    2015-01-01

    Full Text Available The objective of this paper is to develop a novel wind speed forecasting technique, which produces more accurate prediction. The Wavelet Transform (WT along with the Auto Regressive Moving Average (ARMA is chosen to form a hybrid whose combination is expected to give minimum Mean Absolute Prediction Error (MAPE. A simulation study has been conducted by comparing the forecasting results using the Wavelet-ARMA with the ARMA and Artificial Neural Network (ANN-Ensemble Kalman Filter (EnKF hybrid technique to verify the effectiveness of the proposed hybrid method. Results of the proposed hybrid show significant improvements in the forecasting error.

  5. Day-Ahead Wind Speed Forecasting Using Relevance Vector Machine

    Directory of Open Access Journals (Sweden)

    Guoqiang Sun

    2014-01-01

    Full Text Available With the development of wind power technology, the security of the power system, power quality, and stable operation will meet new challenges. So, in this paper, we propose a recently developed machine learning technique, relevance vector machine (RVM, for day-ahead wind speed forecasting. We combine Gaussian kernel function and polynomial kernel function to get mixed kernel for RVM. Then, RVM is compared with back propagation neural network (BP and support vector machine (SVM for wind speed forecasting in four seasons in precision and velocity; the forecast results demonstrate that the proposed method is reasonable and effective.

  6. 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.

  7. Hit Rate of Space Weather Forecasts of the Japanese Forecast Center and Analysis of Problematic Events on the Forecasts between June 2014 and March 2015

    Science.gov (United States)

    Watari, S.; Kato, H.; Yamamoto, K.

    2015-12-01

    The hit rate of space weather forecasts issued by the Japanese forecast center in the National Institute of Information and Communications Technology (NICT) between June 2014 and March 2015 are compared with that by the persistence method. It is shown that the hit rate of the forecasts by the Japanese center is better than that by the persistence method. Several problematic events on the space weather forecasts during the same period are analyzed. Those events are (1) geomagnetic storms associated with coronal mass ejections (CMEs) on 9 September 2014 and on 15 March 2015 with different durations of southward interplanetary magnetic field (IMF), (2) a large active region, AR 12192 without CMEs, solar energetic particle events, and geomagnetic storms, (3) a geomagnetic storm on 7 January 2015 caused by a faint CME, and (4) disagreement between the in-situ observation at 1 AU and the prediction of the Potential Field Source Surface (PFSS) model on timing of sector crossing in January 2015.

  8. Global Ensemble Forecast System (GEFS) [1 Deg.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Global Ensemble Forecast System (GEFS) is a weather forecast model made up of 21 separate forecasts, or ensemble members. The National Centers for Environmental...

  9. Improving Local Weather Forecasts for Agricultural Applications

    NARCIS (Netherlands)

    Doeswijk, T.G.; Keesman, K.J.

    2005-01-01

    For controlling agricultural systems, weather forecasts can be of substantial importance. Studies have shown that forecast errors can be reduced in terms of bias and standard deviation using forecasts and meteorological measurements from one specific meteorological station. For agricultural systems

  10. Short-term spatio-temporal wind power forecast in robust look-ahead power system dispatch

    KAUST Repository

    Xie, Le

    2014-01-01

    We propose a novel statistical wind power forecast framework, which leverages the spatio-temporal correlation in wind speed and direction data among geographically dispersed wind farms. Critical assessment of the performance of spatio-temporal wind power forecast is performed using realistic wind farm data from West Texas. It is shown that spatio-temporal wind forecast models are numerically efficient approaches to improving forecast quality. By reducing uncertainties in near-term wind power forecasts, the overall cost benefits on system dispatch can be quantified. We integrate the improved forecast with an advanced robust look-ahead dispatch framework. This integrated forecast and economic dispatch framework is tested in a modified IEEE RTS 24-bus system. Numerical simulation suggests that the overall generation cost can be reduced by up to 6% using a robust look-ahead dispatch coupled with spatio-temporal wind forecast as compared with persistent wind forecast models. © 2013 IEEE.

  11. Accuracy of prescription drug expenditure forecasts published in the American Journal of Health-System Pharmacy

    Science.gov (United States)

    Hartke, Patricia L.; Vermeulen, Lee C.; Hoffman, James M.; Shah, Nilay D.; Doloresco, Fred; Suda, Katie J.; Li, Edward C.; Matusiak, Linda M.; Hunkler, Robert J.

    2015-01-01

    Purpose To evaluate the accuracy of the forecast of drug expenditures in nonfederal hospitals and clinics published annually in the American Journal of Health-System Pharmacy (AJHP) compared to the drug expenditure forecasts produced annually by the Centers for Medicare and Medicaid Services (CMS). Methods The forecasted drug expenditure growth published in AJHP for nonfederal hospitals (for the years 2003 to 2013) and clinics (for the years 2004 to 2013) was compared to the actual growth each year. The actual and forecasted growth published by CMS was analyzed for the years 2003 to 2012. The mean absolute error (MAE) and directional accuracy for the AJHP forecasts for nonfederal hospitals and clinics, and for the CMS forecasts, were determined and compared. Results Actual growth was within the forecasted range in 2 of 11 years for nonfederal hospitals, and in 3 of 10 years for clinics. The forecasts were directionally accurate 27.3% and 60.0% of the time for nonfederal hospitals and clinics, respectively. The MAE for the nonfederal hospital and clinic drug expenditure forecasts were 2.0 and 4.7 percentage points, respectively. The CMS forecasts were directionally accurate 70% of the time, and the MAE was 2.2 percentage points and was not statistically different than the AJHP forecasts. Conclusion The forecasts published annually in AJHP have comparable accuracy that by CMS for predicting prescription expenditure growth. The forecast paper provides an overview of current trends, which must be combined with local information to accurately forecast institutional drug expenditures. PMID:26386105

  12. Tool for Forecasting Cool-Season Peak Winds Across Kennedy Space Center and Cape Canaveral Air Force Station

    Science.gov (United States)

    Barrett, Joe H., III; Roeder, William P.

    2010-01-01

    compared using an independent verification data set. The two methods were compared to climatology, wind warnings and advisories issued by the 45 WS, and North American Mesoscale (NAM) model (MesoNAM) forecast winds. The performance of the Phase I and II methods were similar with respect to mean absolute error. Since the Phase I data were not stratified by precipitation, this method's peak wind forecasts had a large negative bias on days with precipitation and a small positive bias on days with no precipitation. Overall, the climatology methods performed the worst while the MesoNAM performed the best. Since the MesoNAM winds were the most accurate in the comparison, the final version of the tool was based on the MesoNAM winds. The probability the peak wind will meet or exceed the warning thresholds were based on the one standard deviation error bars from the linear regression. For example, the linear regression might forecast the most likely peak speed to be 35 kt and the error bars used to calculate that the probability of >= 25 kt = 76%, the probability of >= 35 kt = 50%, and the probability of >= 50 kt = 19%. The authors have not seen this application of linear regression error bars in any other meteorological applications. Although probability forecast tools should usually be developed with logistic regression, this technique could be easily generalized to any linear regression forecast tool to estimate the probability of exceeding any desired threshold . This could be useful for previously developed linear regression forecast tools or new forecast applications where statistical analysis software to perform logistic regression is not available. The tool was delivered in two formats - a Microsoft Excel GUI and a Tool Command Language/Tool Kit (Tcl/Tk) GUI in the Meteorological Interactive Data Display System (MIDDS). The Microsoft Excel GUI reads a MesoNAM text file containing hourly forecasts from 0 to 84 hours, from one model run (00 or 12 UTC). The GUI then displays e

  13. Convective Weather Avoidance with Uncertain Weather Forecasts

    Science.gov (United States)

    Karahan, Sinan; Windhorst, Robert D.

    2009-01-01

    Convective weather events have a disruptive impact on air traffic both in terminal area and in en-route airspaces. In order to make sure that the national air transportation system is safe and efficient, it is essential to respond to convective weather events effectively. Traffic flow control initiatives in response to convective weather include ground delay, airborne delay, miles-in-trail restrictions as well as tactical and strategic rerouting. The rerouting initiatives can potentially increase traffic density and complexity in regions neighboring the convective weather activity. There is a need to perform rerouting in an intelligent and efficient way such that the disruptive effects of rerouting are minimized. An important area of research is to study the interaction of in-flight rerouting with traffic congestion or complexity and developing methods that quantitatively measure this interaction. Furthermore, it is necessary to find rerouting solutions that account for uncertainties in weather forecasts. These are important steps toward managing complexity during rerouting operations, and the paper is motivated by these research questions. An automated system is developed for rerouting air traffic in order to avoid convective weather regions during the 20- minute - 2-hour time horizon. Such a system is envisioned to work in concert with separation assurance (0 - 20-minute time horizon), and longer term air traffic management (2-hours and beyond) to provide a more comprehensive solution to complexity and safety management. In this study, weather is dynamic and uncertain; it is represented as regions of airspace that pilots are likely to avoid. Algorithms are implemented in an air traffic simulation environment to support the research study. The algorithms used are deterministic but periodically revise reroutes to account for weather forecast updates. In contrast to previous studies, in this study convective weather is represented as regions of airspace that pilots

  14. Guidelines for forecasting energy demand

    International Nuclear Information System (INIS)

    Sonino, T.

    1976-11-01

    Four methodologies for forecasting energy demand are reviewed here after considering the role of energy in the economy and the analysis of energy use in different economic sectors. The special case of Israel is considered throughout, and some forecasts for energy demands in the year 2000 are presented. An energy supply mix that may be considered feasible is proposed. (author)

  15. Forecasting Using Random Subspace Methods

    NARCIS (Netherlands)

    T. Boot (Tom); D. Nibbering (Didier)

    2016-01-01

    textabstractRandom subspace methods are a novel approach to obtain accurate forecasts in high-dimensional regression settings. We provide a theoretical justification of the use of random subspace methods and show their usefulness when forecasting monthly macroeconomic variables. We focus on two

  16. Now, Here's the Weather Forecast...

    Science.gov (United States)

    Richardson, Mathew

    2013-01-01

    The Met Office has a long history of weather forecasting, creating tailored weather forecasts for customers across the world. Based in Exeter, the Met Office is also home to the Met Office Hadley Centre, a world-leading centre for the study of climate change and its potential impacts. Climate information from the Met Office Hadley Centre is used…

  17. Forecasting Macroeconomic Labour Market Flows

    DEFF Research Database (Denmark)

    Wilke, Ralf

    2017-01-01

    Forecasting labour market flows is important for budgeting and decision-making in government departments and public administration. Macroeconomic forecasts are normally obtained from time series data. In this article, we follow another approach that uses individual-level statistical analysis...

  18. Regional-seasonal weather forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Abarbanel, H.; Foley, H.; MacDonald, G.; Rothaus, O.; Rudermann, M.; Vesecky, J.

    1980-08-01

    In the interest of allocating heating fuels optimally, the state-of-the-art for seasonal weather forecasting is reviewed. A model using an enormous data base of past weather data is contemplated to improve seasonal forecasts, but present skills do not make that practicable. 90 references. (PSB)

  19. Forecasting droughts in West Africa: Operational practice and refined seasonal precipitation forecasts

    Science.gov (United States)

    Bliefernicht, Jan; Siegmund, Jonatan; Seidel, Jochen; Arnold, Hanna; Waongo, Moussa; Laux, Patrick; Kunstmann, Harald

    2016-04-01

    Precipitation forecasts for the upcoming rainy seasons are one of the most important sources of information for an early warning of droughts and water scarcity in West Africa. The meteorological services in West Africa perform seasonal precipitation forecasts within the framework of PRESAO (the West African climate outlook forum) since the end of the 1990s. Various sources of information and statistical techniques are used by the individual services to provide a harmonized seasonal precipitation forecasts for decision makers in West Africa. In this study, we present a detailed overview of the operational practice in West Africa including a first statistical assessment of the performance of the precipitation forecasts for drought situations for the past 18 years (1998 to 2015). In addition, a long-term hindcasts (1982 to 2009) and a semi-operational experiment for the rainy season 2013 using statistical and/or dynamical downscaling are performed to refine the precipitation forecasts from the Climate Forecast System Version 2 (CFSv2), a global ensemble prediction system. This information is post-processed to provide user-oriented precipitation indices such as the onset of the rainy season for supporting water and land use management for rain-fed agriculture. The evaluation of the individual techniques is performed focusing on water-scarce regions of the Volta basin in Burkina Faso and Ghana. The forecasts of the individual techniques are compared to state-of-the-art global observed precipitation products and a novel precipitation database based on long-term daily rain-gage measurements provided by the national meteorological services. The statistical assessment of the PRESAO forecasts indicates skillful seasonal precipitation forecasts for many locations in the Volta basin, particularly for years with water deficits. The operational experiment for the rainy season 2013 illustrates the high potential of a physically-based downscaling for this region but still shows

  20. Application of the North American Multi-Model Ensemble to seasonal water supply forecasting in the Great Lakes basin through the use of the Great Lakes Seasonal Climate Forecast Tool

    Science.gov (United States)

    Gronewold, A.; Apps, D.; Fry, L. M.; Bolinger, R.

    2017-12-01

    The U.S. Army Corps of Engineers (USACE) contribution to the internationally coordinated 6-month forecast of Great Lakes water levels relies on several water supply models, including a regression model relating a coming month's water supply to past water supplies, previous months' precipitation and temperature, and forecasted precipitation and temperature. Probabilistic forecasts of precipitation and temperature depicted in the Climate Prediction Center's seasonal outlook maps are considered to be standard for use in operational forecasting for seasonal time horizons, and have provided the basis for computing a coming month's precipitation and temperature for use in the USACE water supply regression models. The CPC outlook maps are a useful forecast product offering insight into interpretation of climate models through the prognostic discussion and graphical forecasts. However, recent evolution of USACE forecast procedures to accommodate automated data transfer and manipulation offers a new opportunity for direct incorporation of ensemble climate forecast data into probabilistic outlooks of water supply using existing models that have previously been implemented in a deterministic fashion. We will present results from a study investigating the potential for applying data from the North American Multi-Model Ensemble to operational water supply forecasts. The use of NMME forecasts is facilitated by a new, publicly available, Great Lakes Seasonal Climate Forecast Tool that provides operational forecasts of monthly average temperatures and monthly total precipitation summarized for each lake basin.

  1. Effective Feature Preprocessing for Time Series Forecasting

    DEFF Research Database (Denmark)

    Zhao, Junhua; Dong, Zhaoyang; Xu, Zhao

    2006-01-01

    Time series forecasting is an important area in data mining research. Feature preprocessing techniques have significant influence on forecasting accuracy, therefore are essential in a forecasting model. Although several feature preprocessing techniques have been applied in time series forecasting...... performance in time series forecasting. It is demonstrated in our experiment that, effective feature preprocessing can significantly enhance forecasting accuracy. This research can be a useful guidance for researchers on effectively selecting feature preprocessing techniques and integrating them with time...

  2. Method of forecasting power distribution

    International Nuclear Information System (INIS)

    Kaneto, Kunikazu.

    1981-01-01

    Purpose: To obtain forecasting results at high accuracy by reflecting the signals from neutron detectors disposed in the reactor core on the forecasting results. Method: An on-line computer transfers, to a simulator, those process data such as temperature and flow rate for coolants in each of the sections and various measuring signals such as control rod positions from the nuclear reactor. The simulator calculates the present power distribution before the control operation. The signals from the neutron detectors at each of the positions in the reactor core are estimated from the power distribution and errors are determined based on the estimated values and the measured values to determine the smooth error distribution in the axial direction. Then, input conditions at the time to be forecast are set by a data setter. The simulator calculates the forecast power distribution after the control operation based on the set conditions. The forecast power distribution is corrected using the error distribution. (Yoshino, Y.)

  3. Forecasting monthly peak demand of electricity in India—A critique

    International Nuclear Information System (INIS)

    Rallapalli, Srinivasa Rao; Ghosh, Sajal

    2012-01-01

    The nature of electricity differs from that of other commodities since electricity is a non-storable good and there have been significant seasonal and diurnal variations of demand. Under such condition, precise forecasting of demand for electricity should be an integral part of the planning process as this enables the policy makers to provide directions on cost-effective investment and on scheduling the operation of the existing and new power plants so that the supply of electricity can be made adequate enough to meet the future demand and its variations. Official load forecasting in India done by Central Electricity Authority (CEA) is often criticized for being overestimated due to inferior techniques used for forecasting. This paper tries to evaluate monthly peak demand forecasting performance predicted by CEA using trend method and compare it with those predicted by Multiplicative Seasonal Autoregressive Integrated Moving Average (MSARIMA) model. It has been found that MSARIMA model outperforms CEA forecasts both in-sample static and out-of-sample dynamic forecast horizons in all five regional grids in India. For better load management and grid discipline, this study suggests employing sophisticated techniques like MSARIMA for peak load forecasting in India. - Highlights: ► This paper evaluates monthly peak demand forecasting performance by CEA. ► Compares CEA forecasts it with those predicted by MSARIMA model. ► MSARIMA model outperforms CEA forecasts in all five regional grids in India. ► Opportunity exists to improve the performance of CEA forecasts.

  4. Role of hybrid forecasting techniques for transportation planning of broiler meat under uncertain demand in thailand

    Directory of Open Access Journals (Sweden)

    Thoranin Sujjaviriyasup

    2014-12-01

    Full Text Available One of numerous problems experiencing in supply chain management is the demand. Most demands are appeared in terms of uncertainty. The broiler meat industry is inevitably encountering the same problem. In this research, hybrid forecasting model of ARIMA and Support Vector Machine (SVMs are developed to forecast broiler meat export. In addition, ARIMA, SVMs, and Moving Average (MA are chosen for comparing the forecasting efficiency. All the forecasting models are tested and validated using the data of Brazil’s export, Canada’s export, and Thailand’s export. The hybrid model provides accuracy of the forecasted values that are 98.71%, 97.50%, and 93.01%, respectively. In addition, the hybrid model presents the least error of all MAE, RMSE, and MAPE comparing with other forecasting models. As forecasted data are applied to transportation planning, the mean absolute percentage error (MAPE of optimal value of forecasted value and actual value is 14.53%. The hybrid forecasting model shows an ability to reduce risk of total cost of transportation when broiler meat export is forecasted by using MA(2, MA(3, ARIMA, and SVM are 50.59%, 60.18%, 68.01%, and 46.55%, respectively. The results indicate that the developed forecasting model is recommended to broiler meat industries’ supply chain decision.

  5. Self-Organizing Maps-based ocean currents forecasting system

    Science.gov (United States)

    Vilibić, Ivica; Šepić, Jadranka; Mihanović, Hrvoje; Kalinić, Hrvoje; Cosoli, Simone; Janeković, Ivica; Žagar, Nedjeljka; Jesenko, Blaž; Tudor, Martina; Dadić, Vlado; Ivanković, Damir

    2016-03-01

    An ocean surface currents forecasting system, based on a Self-Organizing Maps (SOM) neural network algorithm, high-frequency (HF) ocean radar measurements and numerical weather prediction (NWP) products, has been developed for a coastal area of the northern Adriatic and compared with operational ROMS-derived surface currents. The two systems differ significantly in architecture and algorithms, being based on either unsupervised learning techniques or ocean physics. To compare performance of the two methods, their forecasting skills were tested on independent datasets. The SOM-based forecasting system has a slightly better forecasting skill, especially during strong wind conditions, with potential for further improvement when data sets of higher quality and longer duration are used for training.

  6. Significance of changes in medium-range forecast scores

    Directory of Open Access Journals (Sweden)

    Alan J. Geer

    2016-09-01

    Full Text Available The impact of developments in weather forecasting is measured using forecast verification, but many developments, though useful, have impacts of less than 0.5 % on medium-range forecast scores. Chaotic variability in the quality of individual forecasts is so large that it can be hard to achieve statistical significance when comparing these ‘smaller’ developments to a control. For example, with 60 separate forecasts and requiring a 95 % confidence level, a change in quality of the day-5 forecast needs to be larger than 1 % to be statistically significant using a Student's t-test. The first aim of this study is simply to illustrate the importance of significance testing in forecast verification, and to point out the surprisingly large sample sizes that are required to attain significance. The second aim is to see how reliable are current approaches to significance testing, following suspicion that apparently significant results may actually have been generated by chaotic variability. An independent realisation of the null hypothesis can be created using a forecast experiment containing a purely numerical perturbation, and comparing it to a control. With 1885 paired differences from about 2.5 yr of testing, an alternative significance test can be constructed that makes no statistical assumptions about the data. This is used to experimentally test the validity of the normal statistical framework for forecast scores, and it shows that the naive application of Student's t-test does generate too many false positives (i.e. false rejections of the null hypothesis. A known issue is temporal autocorrelation in forecast scores, which can be corrected by an inflation in the size of confidence range, but typical inflation factors, such as those based on an AR(1 model, are not big enough and they are affected by sampling uncertainty. Further, the importance of statistical multiplicity has not been appreciated, and this becomes particularly dangerous

  7. Human-model hybrid Korean air quality forecasting system.

    Science.gov (United States)

    Chang, Lim-Seok; Cho, Ara; Park, Hyunju; Nam, Kipyo; Kim, Deokrae; Hong, Ji-Hyoung; Song, Chang-Keun

    2016-09-01

    The Korean national air quality forecasting system, consisting of the Weather Research and Forecasting, the Sparse Matrix Operator Kernel Emissions, and the Community Modeling and Analysis (CMAQ), commenced from August 31, 2013 with target pollutants of particulate matters (PM) and ozone. Factors contributing to PM forecasting accuracy include CMAQ inputs of meteorological field and emissions, forecasters' capacity, and inherent CMAQ limit. Four numerical experiments were conducted including two global meteorological inputs from the Global Forecast System (GFS) and the Unified Model (UM), two emissions from the Model Intercomparison Study Asia (MICS-Asia) and the Intercontinental Chemical Transport Experiment (INTEX-B) for the Northeast Asia with Clear Air Policy Support System (CAPSS) for South Korea, and data assimilation of the Monitoring Atmospheric Composition and Climate (MACC). Significant PM underpredictions by using both emissions were found for PM mass and major components (sulfate and organic carbon). CMAQ predicts PM2.5 much better than PM10 (NMB of PM2.5: -20~-25%, PM10: -43~-47%). Forecasters' error usually occurred at the next day of high PM event. Once CMAQ fails to predict high PM event the day before, forecasters are likely to dismiss the model predictions on the next day which turns out to be true. The best combination of CMAQ inputs is the set of UM global meteorological field, MICS-Asia and CAPSS 2010 emissions with the NMB of -12.3%, the RMSE of 16.6μ/m(3) and the R(2) of 0.68. By using MACC data as an initial and boundary condition, the performance skill of CMAQ would be improved, especially in the case of undefined coarse emission. A variety of methods such as ensemble and data assimilation are considered to improve further the accuracy of air quality forecasting, especially for high PM events to be comparable to for all cases. The growing utilization of the air quality forecast induced the public strongly to demand that the accuracy of the

  8. Precipitation forecasts and their uncertainty as input into hydrological models

    Directory of Open Access Journals (Sweden)

    M. Kobold

    2005-01-01

    Full Text Available Torrential streams and fast runoff are characteristic of most Slovenian rivers and extensive damage is caused almost every year by rainstorms affecting different regions of Slovenia. Rainfall-runoff models which are tools for runoff calculation can be used for flood forecasting. In Slovenia, the lag time between rainfall and runoff is only a few hours and on-line data are used only for now-casting. Predicted precipitation is necessary in flood forecasting some days ahead. The ECMWF (European Centre for Medium-Range Weather Forecasts model gives general forecasts several days ahead while more detailed precipitation data with the ALADIN/SI model are available for two days ahead. Combining the weather forecasts with the information on catchment conditions and a hydrological forecasting model can give advance warning of potential flooding notwithstanding a certain degree of uncertainty in using precipitation forecasts based on meteorological models. Analysis of the sensitivity of the hydrological model to the rainfall error has shown that the deviation in runoff is much larger than the rainfall deviation. Therefore, verification of predicted precipitation for large precipitation events was performed with the ECMWF model. Measured precipitation data were interpolated on a regular grid and compared with the results from the ECMWF model. The deviation in predicted precipitation from interpolated measurements is shown with the model bias resulting from the inability of the model to predict the precipitation correctly and a bias for horizontal resolution of the model and natural variability of precipitation.

  9. Technical note: Combining quantile forecasts and predictive distributions of streamflows

    Science.gov (United States)

    Bogner, Konrad; Liechti, Katharina; Zappa, Massimiliano

    2017-11-01

    The enhanced availability of many different hydro-meteorological modelling and forecasting systems raises the issue of how to optimally combine this great deal of information. Especially the usage of deterministic and probabilistic forecasts with sometimes widely divergent predicted future streamflow values makes it even more complicated for decision makers to sift out the relevant information. In this study multiple streamflow forecast information will be aggregated based on several different predictive distributions, and quantile forecasts. For this combination the Bayesian model averaging (BMA) approach, the non-homogeneous Gaussian regression (NGR), also known as the ensemble model output statistic (EMOS) techniques, and a novel method called Beta-transformed linear pooling (BLP) will be applied. By the help of the quantile score (QS) and the continuous ranked probability score (CRPS), the combination results for the Sihl River in Switzerland with about 5 years of forecast data will be compared and the differences between the raw and optimally combined forecasts will be highlighted. The results demonstrate the importance of applying proper forecast combination methods for decision makers in the field of flood and water resource management.

  10. Improving wave forecasting by integrating ensemble modelling and machine learning

    Science.gov (United States)

    O'Donncha, F.; Zhang, Y.; James, S. C.

    2017-12-01

    Modern smart-grid networks use technologies to instantly relay information on supply and demand to support effective decision making. Integration of renewable-energy resources with these systems demands accurate forecasting of energy production (and demand) capacities. For wave-energy converters, this requires wave-condition forecasting to enable estimates of energy production. Current operational wave forecasting systems exhibit substantial errors with wave-height RMSEs of 40 to 60 cm being typical, which limits the reliability of energy-generation predictions thereby impeding integration with the distribution grid. In this study, we integrate physics-based models with statistical learning aggregation techniques that combine forecasts from multiple, independent models into a single "best-estimate" prediction of the true state. The Simulating Waves Nearshore physics-based model is used to compute wind- and currents-augmented waves in the Monterey Bay area. Ensembles are developed based on multiple simulations perturbing input data (wave characteristics supplied at the model boundaries and winds) to the model. A learning-aggregation technique uses past observations and past model forecasts to calculate a weight for each model. The aggregated forecasts are compared to observation data to quantify the performance of the model ensemble and aggregation techniques. The appropriately weighted ensemble model outperforms an individual ensemble member with regard to forecasting wave conditions.

  11. Quantile Forecasting of Wind Power Using Variability Indices

    Directory of Open Access Journals (Sweden)

    Patrick McSharry

    2013-02-01

    Full Text Available Wind power forecasting techniques have received substantial attention recently due to the increasing penetration of wind energy in national power systems. While the initial focus has been on point forecasts, the need to quantify forecast uncertainty and communicate the risk of extreme ramp events has led to an interest in producing probabilistic forecasts. Using four years of wind power data from three wind farms in Denmark, we develop quantile regression models to generate short-term probabilistic forecasts from 15 min up to six hours ahead. More specifically, we investigate the potential of using various variability indices as explanatory variables in order to include the influence of changing weather regimes. These indices are extracted from the same wind power series and optimized specifically for each quantile. The forecasting performance of this approach is compared with that of appropriate benchmark models. Our results demonstrate that variability indices can increase the overall skill of the forecasts and that the level of improvement depends on the specific quantile.

  12. Forecasting project schedule performance using probabilistic and deterministic models

    Directory of Open Access Journals (Sweden)

    S.A. Abdel Azeem

    2014-04-01

    Full Text Available Earned value management (EVM was originally developed for cost management and has not widely been used for forecasting project duration. In addition, EVM based formulas for cost or schedule forecasting are still deterministic and do not provide any information about the range of possible outcomes and the probability of meeting the project objectives. The objective of this paper is to develop three models to forecast the estimated duration at completion. Two of these models are deterministic; earned value (EV and earned schedule (ES models. The third model is a probabilistic model and developed based on Kalman filter algorithm and earned schedule management. Hence, the accuracies of the EV, ES and Kalman Filter Forecasting Model (KFFM through the different project periods will be assessed and compared with the other forecasting methods such as the Critical Path Method (CPM, which makes the time forecast at activity level by revising the actual reporting data for each activity at a certain data date. A case study project is used to validate the results of the three models. Hence, the best model is selected based on the lowest average percentage of error. The results showed that the KFFM developed in this study provides probabilistic prediction bounds of project duration at completion and can be applied through the different project periods with smaller errors than those observed in EV and ES forecasting models.

  13. Model-free aftershock forecasts constructed from similar sequences in the past

    Science.gov (United States)

    van der Elst, N.; Page, M. T.

    2017-12-01

    The basic premise behind aftershock forecasting is that sequences in the future will be similar to those in the past. Forecast models typically use empirically tuned parametric distributions to approximate past sequences, and project those distributions into the future to make a forecast. While parametric models do a good job of describing average outcomes, they are not explicitly designed to capture the full range of variability between sequences, and can suffer from over-tuning of the parameters. In particular, parametric forecasts may produce a high rate of "surprises" - sequences that land outside the forecast range. Here we present a non-parametric forecast method that cuts out the parametric "middleman" between training data and forecast. The method is based on finding past sequences that are similar to the target sequence, and evaluating their outcomes. We quantify similarity as the Poisson probability that the observed event count in a past sequence reflects the same underlying intensity as the observed event count in the target sequence. Event counts are defined in terms of differential magnitude relative to the mainshock. The forecast is then constructed from the distribution of past sequences outcomes, weighted by their similarity. We compare the similarity forecast with the Reasenberg and Jones (RJ95) method, for a set of 2807 global aftershock sequences of M≥6 mainshocks. We implement a sequence-specific RJ95 forecast using a global average prior and Bayesian updating, but do not propagate epistemic uncertainty. The RJ95 forecast is somewhat more precise than the similarity forecast: 90% of observed sequences fall within a factor of two of the median RJ95 forecast value, whereas the fraction is 85% for the similarity forecast. However, the surprise rate is much higher for the RJ95 forecast; 10% of observed sequences fall in the upper 2.5% of the (Poissonian) forecast range. The surprise rate is less than 3% for the similarity forecast. The similarity

  14. A national-scale seasonal hydrological forecast system: development and evaluation over Britain

    Directory of Open Access Journals (Sweden)

    V. A. Bell

    2017-09-01

    Full Text Available Skilful winter seasonal predictions for the North Atlantic circulation and northern Europe have now been demonstrated and the potential for seasonal hydrological forecasting in the UK is now being explored. One of the techniques being used combines seasonal rainfall forecasts provided by operational weather forecast systems with hydrological modelling tools to provide estimates of seasonal mean river flows up to a few months ahead. The work presented here shows how spatial information contained in a distributed hydrological model typically requiring high-resolution (daily or better rainfall data can be used to provide an initial condition for a much simpler forecast model tailored to use low-resolution monthly rainfall forecasts. Rainfall forecasts (hindcasts from the GloSea5 model (1996 to 2009 are used to provide the first assessment of skill in these national-scale flow forecasts. The skill in the combined modelling system is assessed for different seasons and regions of Britain, and compared to what might be achieved using other approaches such as use of an ensemble of historical rainfall in a hydrological model, or a simple flow persistence forecast. The analysis indicates that only limited forecast skill is achievable for Spring and Summer seasonal hydrological forecasts; however, Autumn and Winter flows can be reasonably well forecast using (ensemble mean rainfall forecasts based on either GloSea5 forecasts or historical rainfall (the preferred type of forecast depends on the region. Flow forecasts using ensemble mean GloSea5 rainfall perform most consistently well across Britain, and provide the most skilful forecasts overall at the 3-month lead time. Much of the skill (64 % in the 1-month ahead seasonal flow forecasts can be attributed to the hydrological initial condition (particularly in regions with a significant groundwater contribution to flows, whereas for the 3-month ahead lead time, GloSea5 forecasts account for  ∼ 70

  15. A national-scale seasonal hydrological forecast system: development and evaluation over Britain

    Science.gov (United States)

    Bell, Victoria A.; Davies, Helen N.; Kay, Alison L.; Brookshaw, Anca; Scaife, Adam A.

    2017-09-01

    Skilful winter seasonal predictions for the North Atlantic circulation and northern Europe have now been demonstrated and the potential for seasonal hydrological forecasting in the UK is now being explored. One of the techniques being used combines seasonal rainfall forecasts provided by operational weather forecast systems with hydrological modelling tools to provide estimates of seasonal mean river flows up to a few months ahead. The work presented here shows how spatial information contained in a distributed hydrological model typically requiring high-resolution (daily or better) rainfall data can be used to provide an initial condition for a much simpler forecast model tailored to use low-resolution monthly rainfall forecasts. Rainfall forecasts (hindcasts) from the GloSea5 model (1996 to 2009) are used to provide the first assessment of skill in these national-scale flow forecasts. The skill in the combined modelling system is assessed for different seasons and regions of Britain, and compared to what might be achieved using other approaches such as use of an ensemble of historical rainfall in a hydrological model, or a simple flow persistence forecast. The analysis indicates that only limited forecast skill is achievable for Spring and Summer seasonal hydrological forecasts; however, Autumn and Winter flows can be reasonably well forecast using (ensemble mean) rainfall forecasts based on either GloSea5 forecasts or historical rainfall (the preferred type of forecast depends on the region). Flow forecasts using ensemble mean GloSea5 rainfall perform most consistently well across Britain, and provide the most skilful forecasts overall at the 3-month lead time. Much of the skill (64 %) in the 1-month ahead seasonal flow forecasts can be attributed to the hydrological initial condition (particularly in regions with a significant groundwater contribution to flows), whereas for the 3-month ahead lead time, GloSea5 forecasts account for ˜ 70 % of the forecast

  16. Time Series Forecasting of the Number of Malaysia Airlines and AirAsia Passengers

    Science.gov (United States)

    Asrah, N. M.; Nor, M. E.; Rahim, S. N. A.; Leng, W. K.

    2018-04-01

    The standard practice in forecasting process involved by fitting a model and further analysis on the residuals. If we know the distributional behaviour of the time series data, it can help us to directly analyse the model identification, parameter estimation, and model checking. In this paper, we want to compare the distributional behaviour data from the number of Malaysia Airlines (MAS) and AirAsia passenger’s. From the previous research, the AirAsia passengers are govern by geometric Brownian motion (GBM). The data were normally distributed, stationary and independent. Then, GBM was used to forecast the number of AirAsia passenger’s. The same methods were applied to MAS data and the results then were compared. Unfortunately, the MAS data were not govern by GBM. Then, the standard approach in time series forecasting will be applied to MAS data. From this comparison, we can conclude that the number of AirAsia passengers are always in peak season rather than MAS passengers.

  17. Assessment of storm forecast

    DEFF Research Database (Denmark)

    Cutululis, Nicolaos Antonio; Hahmann, Andrea N.; Huus Bjerge, Martin

    at analysing the ability of existing forecast tools to predict storms at the Horns Rev 2 wind farm. The focus will be on predicting the time where the wind turbine will need to shut down to protect itself, e.g. the time where wind speed exceeds 25 m/s. At the same time, the planned shut-down should cost...... storms was analysed based on historical meteorological data available at Risø DTU and dynamically down-scaled to the Horns Rev 2 wind farm level. This solution was chosen due to the lack of measurements. Moreover, since the project started, there were four events during which Horns Rev 2 wind farm...

  18. Demand Forecasting in the Fashion Industry: A Review

    Directory of Open Access Journals (Sweden)

    Maria Elena Nenni

    2013-08-01

    Full Text Available Forecasting demand is a crucial issue for driving efficient operations management plans. This is especially the case in the fashion industry, where demand uncertainty, lack of historical data and seasonal trends usually coexist. Many approaches to this issue have been proposed in the literature over the past few decades. In this paper, forecasting methods are compared with the aim of linking approaches to the market features.

  19. Development and testing of improved statistical wind power forecasting methods.

    Energy Technology Data Exchange (ETDEWEB)

    Mendes, J.; Bessa, R.J.; Keko, H.; Sumaili, J.; Miranda, V.; Ferreira, C.; Gama, J.; Botterud, A.; Zhou, Z.; Wang, J. (Decision and Information Sciences); (INESC Porto)

    2011-12-06

    (with spatial and/or temporal dependence). Statistical approaches to uncertainty forecasting basically consist of estimating the uncertainty based on observed forecasting errors. Quantile regression (QR) is currently a commonly used approach in uncertainty forecasting. In Chapter 3, we propose new statistical approaches to the uncertainty estimation problem by employing kernel density forecast (KDF) methods. We use two estimators in both offline and time-adaptive modes, namely, the Nadaraya-Watson (NW) and Quantilecopula (QC) estimators. We conduct detailed tests of the new approaches using QR as a benchmark. One of the major issues in wind power generation are sudden and large changes of wind power output over a short period of time, namely ramping events. In Chapter 4, we perform a comparative study of existing definitions and methodologies for ramp forecasting. We also introduce a new probabilistic method for ramp event detection. The method starts with a stochastic algorithm that generates wind power scenarios, which are passed through a high-pass filter for ramp detection and estimation of the likelihood of ramp events to happen. The report is organized as follows: Chapter 2 presents the results of the application of ITL training criteria to deterministic WPF; Chapter 3 reports the study on probabilistic WPF, including new contributions to wind power uncertainty forecasting; Chapter 4 presents a new method to predict and visualize ramp events, comparing it with state-of-the-art methodologies; Chapter 5 briefly summarizes the main findings and contributions of this report.

  20. Fuzzy logic-based analogue forecasting and hybrid modelling of horizontal visibility

    Science.gov (United States)

    Tuba, Zoltán; Bottyán, Zsolt

    2018-04-01

    Forecasting visibility is one of the greatest challenges in aviation meteorology. At the same time, high accuracy visibility forecasts can significantly reduce or make avoidable weather-related risk in aviation as well. To improve forecasting visibility, this research links fuzzy logic-based analogue forecasting and post-processed numerical weather prediction model outputs in hybrid forecast. Performance of analogue forecasting model was improved by the application of Analytic Hierarchy Process. Then, linear combination of the mentioned outputs was applied to create ultra-short term hybrid visibility prediction which gradually shifts the focus from statistical to numerical products taking their advantages during the forecast period. It gives the opportunity to bring closer the numerical visibility forecast to the observations even it is wrong initially. Complete verification of categorical forecasts was carried out; results are available for persistence and terminal aerodrome forecasts (TAF) as well in order to compare. The average value of Heidke Skill Score (HSS) of examined airports of analogue and hybrid forecasts shows very similar results even at the end of forecast period where the rate of analogue prediction in the final hybrid output is 0.1-0.2 only. However, in case of poor visibility (1000-2500 m), hybrid (0.65) and analogue forecasts (0.64) have similar average of HSS in the first 6 h of forecast period, and have better performance than persistence (0.60) or TAF (0.56). Important achievement that hybrid model takes into consideration physics and dynamics of the atmosphere due to the increasing part of the numerical weather prediction. In spite of this, its performance is similar to the most effective visibility forecasting methods and does not follow the poor verification results of clearly numerical outputs.

  1. Midterm Electricity Market Clearing Price Forecasting Using Two-Stage Multiple Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Xing Yan

    2015-01-01

    Full Text Available Currently, there are many techniques available for short-term forecasting of the electricity market clearing price (MCP, but very little work has been done in the area of midterm forecasting of the electricity MCP. The midterm forecasting of the electricity MCP is essential for maintenance scheduling, planning, bilateral contracting, resources reallocation, and budgeting. A two-stage multiple support vector machine (SVM based midterm forecasting model of the electricity MCP is proposed in this paper. The first stage is utilized to separate the input data into corresponding price zones by using a single SVM. Then, the second stage is applied utilizing four parallel designed SVMs to forecast the electricity price in four different price zones. Compared to the forecasting model using a single SVM, the proposed model showed improved forecasting accuracy in both peak prices and overall system. PJM interconnection data are used to test the proposed model.

  2. Short-range quantitative precipitation forecasting using Deep Learning approaches

    Science.gov (United States)

    Akbari Asanjan, A.; Yang, T.; Gao, X.; Hsu, K. L.; Sorooshian, S.

    2017-12-01

    Predicting short-range quantitative precipitation is very important for flood forecasting, early flood warning and other hydrometeorological purposes. This study aims to improve the precipitation forecasting skills using a recently developed and advanced machine learning technique named Long Short-Term Memory (LSTM). The proposed LSTM learns the changing patterns of clouds from Cloud-Top Brightness Temperature (CTBT) images, retrieved from the infrared channel of Geostationary Operational Environmental Satellite (GOES), using a sophisticated and effective learning method. After learning the dynamics of clouds, the LSTM model predicts the upcoming rainy CTBT events. The proposed model is then merged with a precipitation estimation algorithm termed Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) to provide precipitation forecasts. The results of merged LSTM with PERSIANN are compared to the results of an Elman-type Recurrent Neural Network (RNN) merged with PERSIANN and Final Analysis of Global Forecast System model over the states of Oklahoma, Florida and Oregon. The performance of each model is investigated during 3 storm events each located over one of the study regions. The results indicate the outperformance of merged LSTM forecasts comparing to the numerical and statistical baselines in terms of Probability of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), RMSE and correlation coefficient especially in convective systems. The proposed method shows superior capabilities in short-term forecasting over compared methods.

  3. A New Statistical Model for Eruption Forecasting at Open Conduit Volcanoes: an Application to Mt Etna and Kilauea Volcanoes

    Science.gov (United States)

    Passarelli, Luigi; Sanso, Bruno; Laura, Sandri; Marzocchi, Warner

    2010-05-01

    One of the main goals in volcanology is to forecast volcanic eruptions. A trenchant forecast should be made before the onset of a volcanic eruption, using the data available at that time, with the aim of mitigating the volcanic risk associated to the volcanic event. In other words, models implemented with forecast purposes have to take into account the possibility to provide "forward" forecasts and should avoid the idea of a merely "retrospective" fitting of the data available. In this perspective, the main idea of the present model is to forecast the next volcanic eruption after the end of the last one, using only the data available at that time. We focus our attention on volcanoes with open conduit regime and high eruption frequency. We assume a generalization of the classical time predictable model to describe the eruptive behavior of open conduit volcanoes and we use a Bayesian hierarchical model to make probabilistic forecast. We apply the model to Kilauea volcano eruptive data and Mt. Etna volcano flank eruption data. The aims of this model are: 1) to test whether or not the Kilauea and Mt Etna volcanoes follow a time predictable behavior; 2) to discuss the volcanological implications of the time predictable model parameters inferred; 3) to compare the forecast capabilities of this model with other models present in literature. The results obtained using the MCMC sampling algorithm show that both volcanoes follow a time predictable behavior. The numerical values of the time predictable model parameters inferred suggest that the amount of the erupted volume could change the dynamics of the magma chamber refilling process during the repose period. The probability gain of this model compared with other models already present in literature is appreciably greater than zero. This means that our model performs better forecast than previous models and it could be used in a probabilistic volcanic hazard assessment scheme. In this perspective, the probability of

  4. Spatio-temporal modeling for real-time ozone forecasting.

    Science.gov (United States)

    Paci, Lucia; Gelfand, Alan E; Holland, David M

    2013-05-01

    The accurate assessment of exposure to ambient ozone concentrations is important for informing the public and pollution monitoring agencies about ozone levels that may lead to adverse health effects. High-resolution air quality information can offer significant health benefits by leading to improved environmental decisions. A practical challenge facing the U.S. Environmental Protection Agency (USEPA) is to provide real-time forecasting of current 8-hour average ozone exposure over the entire conterminous United States. Such real-time forecasting is now provided as spatial forecast maps of current 8-hour average ozone defined as the average of the previous four hours, current hour, and predictions for the next three hours. Current 8-hour average patterns are updated hourly throughout the day on the EPA-AIRNow web site. The contribution here is to show how we can substantially improve upon current real-time forecasting systems. To enable such forecasting, we introduce a downscaler fusion model based on first differences of real-time monitoring data and numerical model output. The model has a flexible coefficient structure and uses an efficient computational strategy to fit model parameters. Our hybrid computational strategy blends continuous background updated model fitting with real-time predictions. Model validation analyses show that we are achieving very accurate and precise ozone forecasts.

  5. 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.

  6. The use of various interplanetary scintillation indices within geomagnetic forecasts

    Directory of Open Access Journals (Sweden)

    E. A. Lucek

    Full Text Available Interplanetary scintillation (IPS, the twinkling of small angular diameter radio sources, is caused by the interaction of the signal with small-scale plasma irregularities in the solar wind. The technique may be used to sense remotely the near-Earth heliosphere and observations of a sufficiently large number of sources may be used to track large-scale disturbances as they propagate from close to the Sun to the Earth. Therefore, such observations have potential for use within geomagnetic forecasts. We use daily data from the Mullard Radio Astronomy Observatory, made available through the World Data Centre, to test the success of geomagnetic forecasts based on IPS observations. The approach discussed here was based on the reduction of the information in a map to a single number or series of numbers. The advantages of an index of this nature are that it may be produced routinely and that it could ideally forecast both the occurrence and intensity of geomagnetic activity. We start from an index that has already been described in the literature, INDEX35. On the basis of visual examination of the data in a full skymap format modifications were made to the way in which the index was calculated. It was hoped that these would lead to an improvement in its forecasting ability. Here we assess the forecasting potential of the index using the value of the correlation coefficient between daily Ap and the IPS index, with IPS leading by 1 day. We also compare the forecast based on the IPS index with forecasts of Ap currently released by the Space Environment Services Center (SESC. Although we find that the maximum improvement achieved is small, and does not represent a significant advance in forecasting ability, the IPS forecasts at this phase of the solar cycle are of a similar quality to those made by SESC.

  7. On the clustering of climate models in ensemble seasonal forecasting

    Science.gov (United States)

    Yuan, Xing; Wood, Eric F.

    2012-09-01

    Multi-model ensemble seasonal forecasting system has expanded in recent years, with a dozen coupled climate models around the world being used to produce hindcasts or real-time forecasts. However, many models are sharing similar atmospheric or oceanic components which may result in similar forecasts. This raises questions of whether the ensemble is over-confident if we treat each model equally, or whether we can obtain an effective subset of models that can retain predictability and skill as well. In this study, we use a hierarchical clustering method based on inverse trigonometric cosine function of the anomaly correlation of pairwise model hindcasts to measure the similarities among twelve American and European seasonal forecast models. Though similarities are found between models sharing the same atmospheric component, different versions of models from the same center sometimes produce quite different temperature forecasts, which indicate that detailed physics packages such as radiation and land surface schemes need to be analyzed in interpreting the clustering result. Uncertainties in clustering for different forecast lead times also make reducing redundant models more complicated. Predictability analysis shows that multi-model ensemble is not necessarily better than a single model, while the cluster ensemble shows consistent improvement against individual models. The eight model-based cluster ensemble forecast shows comparable performance to the total twelve model ensemble in terms of probabilistic forecast skill for accuracy and discrimination. This study also manifests that models developed in U.S. and Europe are more independent from each other, suggesting the necessity of international collaboration in enhancing multi-model ensemble seasonal forecasting.

  8. Do we need demographic data to forecast plant population dynamics?

    Science.gov (United States)

    Tredennick, Andrew T.; Hooten, Mevin B.; Adler, Peter B.

    2017-01-01

    Rapid environmental change has generated growing interest in forecasts of future population trajectories. Traditional population models built with detailed demographic observations from one study site can address the impacts of environmental change at particular locations, but are difficult to scale up to the landscape and regional scales relevant to management decisions. An alternative is to build models using population-level data that are much easier to collect over broad spatial scales than individual-level data. However, it is unknown whether models built using population-level data adequately capture the effects of density-dependence and environmental forcing that are necessary to generate skillful forecasts.Here, we test the consequences of aggregating individual responses when forecasting the population states (percent cover) and trajectories of four perennial grass species in a semi-arid grassland in Montana, USA. We parameterized two population models for each species, one based on individual-level data (survival, growth and recruitment) and one on population-level data (percent cover), and compared their forecasting accuracy and forecast horizons with and without the inclusion of climate covariates. For both models, we used Bayesian ridge regression to weight the influence of climate covariates for optimal prediction.In the absence of climate effects, we found no significant difference between the forecast accuracy of models based on individual-level data and models based on population-level data. Climate effects were weak, but increased forecast accuracy for two species. Increases in accuracy with climate covariates were similar between model types.In our case study, percent cover models generated forecasts as accurate as those from a demographic model. For the goal of forecasting, models based on aggregated individual-level data may offer a practical alternative to data-intensive demographic models. Long time series of percent cover data already exist

  9. Solid Waste Integrated Forecast Technical (SWIFT) Report FY 2001 to FY 2046 Volume 1

    International Nuclear Information System (INIS)

    BARCOT, R.A.

    2001-01-01

    In an effort to maintain an up-to-date Solid Waste Forecast, Fluor Hanford, Inc. requested a midyear update to the FY2001 Solid Waste Forecast collected in June 2000. Below is a list of generators reporting significant changes from the baseline FY2001 forecast, as well as approved generators previously not reporting wastes. The updated data was collected in February 2001. The cumulative effect of the changes in the near term was minor. Included in this update are waste generation maps for onsite facilities. The maps are updated annually and use the baseline forecast as a data reference

  10. Should we use seasonnal meteorological ensemble forecasts for hydrological forecasting? A case study for nordic watersheds in Canada.

    Science.gov (United States)

    Bazile, Rachel; Boucher, Marie-Amélie; Perreault, Luc; Leconte, Robert; Guay, Catherine

    2017-04-01

    Hydro-electricity is a major source of energy for many countries throughout the world, including Canada. Long lead-time streamflow forecasts are all the more valuable as they help decision making and dam management. Different techniques exist for long-term hydrological forecasting. Perhaps the most well-known is 'Extended Streamflow Prediction' (ESP), which considers past meteorological scenarios as possible, often equiprobable, future scenarios. In the ESP framework, those past-observed meteorological scenarios (climatology) are used in turn as the inputs of a chosen hydrological model to produce ensemble forecasts (one member corresponding to each year in the available database). Many hydropower companies, including Hydro-Québec (province of Quebec, Canada) use variants of the above described ESP system operationally for long-term operation planning. The ESP system accounts for the hydrological initial conditions and for the natural variability of the meteorological variables. However, it cannot consider the current initial state of the atmosphere. Climate models can help remedy this drawback. In the context of a changing climate, dynamical forecasts issued from climate models seem to be an interesting avenue to improve upon the ESP method and could help hydropower companies to adapt their management practices to an evolving climate. Long-range forecasts from climate models can also be helpful for water management at locations where records of past meteorological conditions are short or nonexistent. In this study, we compare 7-month hydrological forecasts obtained from climate model outputs to an ESP system. The ESP system mimics the one used operationally at Hydro-Québec. The dynamical climate forecasts are produced by the European Center for Medium range Weather Forecasts (ECMWF) System4. Forecasts quality is assessed using numerical scores such as the Continuous Ranked Probability Score (CRPS) and the Ignorance score and also graphical tools such as the

  11. Aerosol Radiative Forcing and Weather Forecasts in the ECMWF Model

    Science.gov (United States)

    Bozzo, A.; Benedetti, A.; Rodwell, M. J.; Bechtold, P.; Remy, S.

    2015-12-01

    Aerosols play an important role in the energy balance of the Earth system via direct scattering and absorpiton of short-wave and long-wave radiation and indirect interaction with clouds. Diabatic heating or cooling by aerosols can also modify the vertical stability of the atmosphere and influence weather pattern with potential impact on the skill of global weather prediction models. The Copernicus Atmosphere Monitoring Service (CAMS) provides operational daily analysis and forecast of aerosol optical depth (AOD) for five aerosol species using a prognostic model which is part of the Integrated Forecasting System of the European Centre for Medium-Range Weather Forecasts (ECMWF-IFS). The aerosol component was developed during the research project Monitoring Atmospheric Composition and Climate (MACC). Aerosols can have a large impact on the weather forecasts in case of large aerosol concentrations as found during dust storms or strong pollution events. However, due to its computational burden, prognostic aerosols are not yet feasible in the ECMWF operational weather forecasts, and monthly-mean climatological fields are used instead. We revised the aerosol climatology used in the operational ECMWF IFS with one derived from the MACC reanalysis. We analyse the impact of changes in the aerosol radiative effect on the mean model climate and in medium-range weather forecasts, also in comparison with prognostic aerosol fields. The new climatology differs from the previous one by Tegen et al 1997, both in the spatial distribution of the total AOD and the optical properties of each aerosol species. The radiative impact of these changes affects the model mean bias at various spatial and temporal scales. On one hand we report small impacts on measures of large-scale forecast skill but on the other hand details of the regional distribution of aerosol concentration have a large local impact. This is the case for the northern Indian Ocean where the radiative impact of the mineral

  12. Climate Forecast System Version 2 (CFSv2) Operational Forecasts

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Climate Forecast System Version 2 (CFSv2) produced by the NOAA National Centers for Environmental Prediction (NCEP) is a fully coupled model representing the...

  13. Black Sea coastal forecasting system

    Directory of Open Access Journals (Sweden)

    A. I. Kubryakov

    2012-03-01

    Full Text Available The Black Sea coastal nowcasting and forecasting system was built within the framework of EU FP6 ECOOP (European COastalshelf sea OPerational observing and forecasting system project for five regions: the south-western basin along the coasts of Bulgaria and Turkey, the north-western shelf along the Romanian and Ukrainian coasts, coastal zone around of the Crimea peninsula, the north-eastern Russian coastal zone and the coastal zone of Georgia. The system operates in the real-time mode during the ECOOP project and afterwards. The forecasts include temperature, salinity and current velocity fields. Ecosystem model operates in the off-line mode near the Crimea coast.

  14. Forecasts: uncertain, inaccurate and biased?

    DEFF Research Database (Denmark)

    Nicolaisen, Morten Skou; Ambrasaite, Inga; Salling, Kim Bang

    2012-01-01

    Cost Benefit Analysis (CBA) is the dominating methodology for appraisal of transport infrastructure projects across the globe. In order to adequately assess the costs and benefits of such projects two types of forecasts are crucial to the validity of the appraisal. First are the forecasts of cons....... It is recommended that more attention is given to monitoring completed projects so future forecasts can benefit from better data availability through systematic ex-post evaluations, and an example of how to utilize such data in practice is presented....

  15. FORECASTING TOURIST ARRIVALS TO LANGKAWI ISLAND MALAYSIA

    Directory of Open Access Journals (Sweden)

    Kamarul Ariffin MANSOR

    2015-06-01

    Full Text Available Tourism is the act of travelling for a person or group of people from their own locality to a specific destination in a short term or long term period either for leisure or business purposes. Tourism is an important sector in the Malaysian economy where tourism development will lead to the positive economic development of the country and in general improve the quality of life for all citizens. Therefore, forecasting tourist arrivals with high accuracy becomes important since it may ensure the development and the readiness of all tourism related industries such as hotels, transportation, food and services industries and their best shape. This study focuses on tourist arrivals in Langkawi Island as one of the major tourist attractions situated in the northerly region of Peninsular Malaysia. Importantly, this paper attempts to measure and compare the performance of forecasting with Exponential Smoothing, ARIMA and ARFIMA models using the R software package.

  16. Ticket consumption forecast for Brazilian championship games

    Directory of Open Access Journals (Sweden)

    Adriana Bruscato Bortoluzzo

    Full Text Available Abstract For the efficiency of sales and marketing management of athletic clubs, it is crucial to find a way to appropriately estimate the level of demand for sporting events. More precise estimates allow for an appropriate financial and operational plan and a higher quality of service delivered to the fans. The focus of this study is to analyze and forecast the ticket consumption for soccer games in Brazilian stadiums. We compare the results of the regression model with normally distributed errors (benchmark, the TOBIT model and the Gamma generalized linear model. The models include explanatory variables related to the economic environment, product quality, as well as monetary and non-monetary incentives that people are given to attend sporting events at stadiums. We show that most of these variables are statistically significant to explain the amount of fans that go to stadiums. We used different measures of accuracy to evaluate the performance of demand forecasts and concluded that Gamma generalized linear model presented better results to forecast the ticket consumption for Brazilian championship games, when compared to a benchmark.

  17. Magnetogram Forecast: An All-Clear Space Weather Forecasting System

    Science.gov (United States)

    Barghouty, Nasser; Falconer, David

    2015-01-01

    Solar flares and coronal mass ejections (CMEs) are the drivers of severe space weather. Forecasting the probability of their occurrence is critical in improving space weather forecasts. The National Oceanic and Atmospheric Administration (NOAA) currently uses the McIntosh active region category system, in which each active region on the disk is assigned to one of 60 categories, and uses the historical flare rates of that category to make an initial forecast that can then be adjusted by the NOAA forecaster. Flares and CMEs are caused by the sudden release of energy from the coronal magnetic field by magnetic reconnection. It is believed that the rate of flare and CME occurrence in an active region is correlated with the free energy of an active region. While the free energy cannot be measured directly with present observations, proxies of the free energy can instead be used to characterize the relative free energy of an active region. The Magnetogram Forecast (MAG4) (output is available at the Community Coordinated Modeling Center) was conceived and designed to be a databased, all-clear forecasting system to support the operational goals of NASA's Space Radiation Analysis Group. The MAG4 system automatically downloads nearreal- time line-of-sight Helioseismic and Magnetic Imager (HMI) magnetograms on the Solar Dynamics Observatory (SDO) satellite, identifies active regions on the solar disk, measures a free-energy proxy, and then applies forecasting curves to convert the free-energy proxy into predicted event rates for X-class flares, M- and X-class flares, CMEs, fast CMEs, and solar energetic particle events (SPEs). The forecast curves themselves are derived from a sample of 40,000 magnetograms from 1,300 active region samples, observed by the Solar and Heliospheric Observatory Michelson Doppler Imager. Figure 1 is an example of MAG4 visual output

  18. Network bandwidth utilization forecast model on high bandwidth networks

    Energy Technology Data Exchange (ETDEWEB)

    Yoo, Wuchert (William) [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Sim, Alex [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

    2015-03-30

    With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2%. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.

  19. Network Bandwidth Utilization Forecast Model on High Bandwidth Network

    Energy Technology Data Exchange (ETDEWEB)

    Yoo, Wucherl; Sim, Alex

    2014-07-07

    With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2percent. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.

  20. Forecast Share Prices with Artificial Neural Network in Crisis Periods

    Directory of Open Access Journals (Sweden)

    Feyyaz Zeren

    2014-09-01

    Full Text Available Crisis periods present quite a significant moment for financial markets. Considering not losing and changing the crisis periods into opportunities, forecasts of share prices during these periods have an importance for the investors. In this study, daily closing prices of Borsa Istanbul National 100 index during the three big crisis periods, as 1994, 2001, and 2008, have been tried to be forecasted, by using artificial neural networks. As a result of this study, it is determined that in the forecasts of Borsa Istanbul, artificial neural networks show high performance. This result was proved by both comparing the values that occurred and forecasted on the graphics, and Mean Absolute Percentage Error (MAPE calculations