Lin, W.B. [Far East Univ., Tainan, Taiwan (China). Dept. of Electrical Engineering; Lee, C.M. [Nan Kai Inst. of Technology, Nantou, Taiwan (China). Dept. of Computer and Communication Engineering; Shih, K.R. [National Formosa Univ., Yunlin, Taiwan (China). Dept. of Electrical Engineering; Huang, C.H. [National Yunlin Univ. of Science and Technology, Yunlin, Taiwan (China). Graduate School of Engineering Science and Technology
This paper presented a new approach for short term load forecasting that combined the lifting scheme and Box Jenkins technique. The lifting scheme based on multi-revolution analysis (MRA) was used to extract the features and characteristics of load data which stand for low frequency and high frequency components. The lifting scheme is a general and flexible method for the construction of bi-orthogonal wavelets entirely in the spatial domain. It has many advantages over the classical wavelet transform, including fast calculation and in-place implementation of the wavelet transform. As such, forecasting accuracy improved. Based on wavelet MRA, the original load series was decomposed through the lifting scheme into different sub-series at different levels of revolution, which showed the different frequency characteristic of the load. The sub-series was then forecasted by the Box Jenkins seasonal autoregressive integrated moving average model (SARIMA). The proposed method was tested in the hourly load forecast provided by the Taipower Company. A comparison of the proposed method with the Box Jenkins method demonstrated that the proposed method is accurate for short term load forecasting. 21 refs., 3 tabs., 9 figs.
Samira Muhammad Salh
Full Text Available The Auto-regressive model in the time series is regarded one of the statistical articles which is more used because it gives us a simple method to limit the relation between variables time series. More over it is one of Box –Jenkins models to limit the time series in the forecasting the value of phenomenon in the future so that study aims for the practical analysis studying for the auto-regressive models in the time series, through one of Box –Jenkins models for forecasting the daily degrees of temperature in Sulaimani city for the year (2012- Sept.2013 and then for building a sample in the way of special data in the degrees of temperature and its using in the calculating the future forecasting . the style which is used is the descriptive and analyzing by the help of data that is dealt with statistically and which is collected from the official resources To reach his mentioned aim , the discussion of the following items has been done by the theoretical part which includes the idea of time series and its quality and the autocorrelation and Box –Jenkins and then the practical part which includes the statistical analysis for the data and the discussion of the theoretical part, so they reached to a lot of conclusions as it had come in the practical study for building autoregressive models of time series as the mode was very suitable is the auto-regressive model and model moving average by the degree (1,1,1.
Full Text Available This paper presents the Box-Jenkins method used for wind speed prediction. Box-Jenkins methodology finds the best fit of a time series to past values in order to make forecasts. This methodology uses the autocorrelation and partial autocorrelation functions.
Full Text Available Après avoir exposé le problème de prévision qui se posait à la société Elf France, et présenté sa généralisation à la plupart des entreprises, nous définirons la place que tient cette étude dans le vaste débat sur la prévision à court terme actuelle. La recherche d'une méthode appropriée pour la résolution du problème posé passe d'abord par une synthèse de la littérature : après un classement méthodologique des techniques, une analyse critique permet d'effectuer le choix de la méthode Box-Jenkins dont nous proposons une présentation sommaire. La seconde grande partie concerne l'application pratique relative à la prévision et au suivi de nos six séries pétrolières : après avoir résolu le problème par une démarche séquentielle, intégrant progressivement les développements de complexité croissante, une analyse critique évalue les résultats obtenus et propose une extension prometteuse des travaux ainsi que les grands axes de développement pour l'avenir. En dernier lieu, la conclusion apporte sa contribution au débat sur la modélisation à court terme actuelle et définit le rôle pivot que devrait jouer la méthodologie de Box et Jenkins. This article contains two main parts. The first one is theoretical, and the second one is practical. After describing the forecasting problems faced by Elf France and its generalization to most companies, the position this study occupies in the vast debate on current short-term forecasting is defined. The search for a suitable method for solving the problem raised first goes via a synthesis of the literature. After making a methodological classification of techniques, a critical analysis can be used to choose the Box-Jenkins methods which is outlined. The second main part is relative to the practical application concerning forecasting and the following of our six petroleum series. After the problem has been solved by a sequential procedure, progressively integrating
Gaspar Marroquín Martínez; Luis Eduardo Chalita Tovar
Los productos del sector agroalimentario tienen como características económicas distintivas, la alta variabilidad en sus precios. Teniendo en cuenta la incertidumbre de los precios, una posible forma de planificar racionalmente la toma de decisiones, que consiste en elaborar pronósticos confiables del comportamiento futuro de esa variable. En este trabajo se usó la metodología Box-Jenkins, para identificar un modelo econométrico autoregresivo integrado de media móvil (ARIMA), que se ajusta al...
Щелкалин, Виталий Николаевич
This paper presents an overview of the rapidly developing in recent years by the author in various fields of science and technology of modern mathematical models and methods based on joint usage of ideas of the “Caterpillar”-SSA and Box-Jenkins methods. The proposed by author models are a priority at present probabilistic and deterministic nonlinear decomposition models
Previsão de demanda: uma aplicação dos modelos Box-Jenkins na área de assistência técnica de computadores pessoais Demand forecasting: an application of the Box-Jenkins models in the technical assistance of personal computer
Full Text Available A previsão de demanda é uma atividade importante para auxiliar na determinação dos recursos necessários para a empresa. Neste artigo, a metodologia de Box-Jenkins foi utilizada para analisar dados históricos de uma empresa de assistência técnica de computadores pessoais e obter previsões do número de atendimentos. A empresa estudada apresenta três tipos de clientes diferenciados: contratos, garantia e avulsos. Como cada segmento de clientes tem suas peculiaridades, a previsão de demanda foi direcionada a cada tipo, buscando representar o comportamento de tendência e a sazonalidade por meio dos modelos de Box-Jenkins. A obtenção dos modelos mais adequados foi baseada na análise de gráficos e em testes estatísticos próprios da metodologia, os quais subsidiaram a decisão de adotar o modelo AR(1 para prever o número de atendimentos dos clientes tipo contrato, o modelo ARIMA(2,1,0 para os clientes tipo garantia e um modelo sazonal SARIMA(0,1,0(0,1,112 para os clientes tipo avulsos.Demand forecasting is an important tool to aid on the determination of necessary resources of a given company. In this paper, the Box-Jenkins methodology was applied to analyze historical data of a personal computer repair company and provide a forecast for the number of service calls. The company studied presents three segments of clients: contracts, warranty, and on-call. As each client has it own characteristics, in order to better represent tendency and seasonality behavior through the Box-Jenkins models, a specific forecasting model was developed for each segment. The choice of the optimum models were based into graphic analysis and statistical tests, which lead to the decision of adopting the AR(1 model to foresee the number of contract clients, the ARIMA(2,1,0 model for warranty clients and the SARIMA(0,1,0(0,1,112 seasonal model for on-call clients.
Après avoir exposé le problème de prévision qui se posait à la société Elf France, et présenté sa généralisation à la plupart des entreprises, nous définirons la place que tient cette étude dans le vaste débat sur la prévision à court terme actuelle. La recherche d'une méthode appropriée pour la résolution du problème posé passe d'abord par une synthèse de la littérature : après un classement méthodologique des techniques, une analyse critique permet d'effectuer le choix de la méthode Box-Jen...
基于天文辐射和日照率数据,采用系统辨识的方法建立了模拟太阳日总辐射量的BJ( Box-Jenkins)模型.该模型与传统的(A)ngstr(m)的日总辐射量的计算公式相比,模拟和预测效果更好.%Based on astronomical radiation and percentage of possible sunshine data, the BJ (Box-Jenkins) model for daily solar irradiation is established using system identification method. The simulation and the one-step forecast results of the BJ model are better than Angstrm formula of the calculation.
Dilli R Aryal; WANG Yao-wu(王要武)
Time-series analysis is important to a wide range of disciplines transcending both the physical and social sciences for proactive policy decisions. Statistical models have sound theoretical basis and have been successfully used in a number of problem domains in time series forecasting. Due to power and flexibility, Box-Jenkins ARIMA model has gained enormous popularity in many areas and research practice for the last three decades.More recently, the neural networks have been shown to be a promising alternative tool for modeling and forecasting owing to their ability to capture the nonlinearity in the data. However, despite the popularity and the superiority of ARIMA and ANN models, the empirical forecasting performance has been rather mixed so that no single method is best in every situation. In this study, a hybrid ARIMA and neural networks model to time series forecasting is proposed. The basic idea behind the model combination is to use each model's unique features to capture different patterns in the data. With three real data sets, empirical results evidently show that the hybrid model outperforms ARIMA and ANN model noticeably in terms of forecasting accuracy used in isolation.
Jing CHEN; Rui-feng DING
Based on the work in Ding and Ding (2008), we develop a modifi ed stochastic gradient (SG) parameter estimation algorithm for a dual-rate Box-Jenkins model by using an auxiliary model. We simplify the complex dual-rate Box-Jenkins model to two fi nite impulse response (FIR) models, present an auxiliary model to estimate the missing outputs and the unknown noise variables, and compute all the unknown parameters of the system with colored noises. Simulation results indicate that the proposed method is effective.
Astronomical radiation as the input data, the BJ (Box-Jenkins) model for surface solar radiation was established using system identification method. The model was affirmed to be workable by residual analysis and zero-pole test. This method can be used to predict the surface solar radiation of 5 -15 minutes time interval, the prediction results can provide solar radiation data for power output forecast of solar PV power plants.%利用天文辐射作为输入数据,采用系统辨识的方法得到地表太阳辐射的BJ(Box-Jenkins)模型,并通过残 差分析和零极点检验.该方法可用于预测5～ 15min时间间隔的地表太阳辐射,为太阳能电站的功率输出预测提供太阳能辐射数据.
If you are a Jenkins novice or beginner with a basic understanding of continuous integration, then this is the book for you. Beginners in Jenkins will get quick hands-on experience and gain the confidence to go ahead and explore the use of Jenkins further.
Streamline software development with Jenkins, the popular Java-based open source tool that has revolutionized the way teams think about Continuous Integration (CI). This complete guide shows you how to automate your build, integration, release, and deployment processes with Jenkins-and demonstrates how CI can save you time, money, and many headaches. Ideal for developers, software architects, and project managers, Jenkins: The Definitive Guide is both a CI tutorial and a comprehensive Jenkins reference. Through its wealth of best practices and real-world tips, you'll discover how easy it is
Berg, Alan Mark
If you are a Java developer, a software architect, a technical project manager, a build manager, or a development or QA engineer, then this book is ideal for you. A basic understanding of the software development life cycle and Java development is needed, as well as a rudimentary understanding of Jenkins.
Casañola-Martin, Gerardo M; Le-Thi-Thu, Huong; Pérez-Giménez, Facundo; Marrero-Ponce, Yovani; Merino-Sanjuán, Matilde; Abad, Concepción; González-Díaz, Humberto
The ubiquitin-proteasome pathway (UPP) is the primary degradation system of short-lived regulatory proteins. Cellular processes such as the cell cycle, signal transduction, gene expression, DNA repair and apoptosis are regulated by this UPP and dysfunctions in this system have important implications in the development of cancer, neurodegenerative, cardiac and other human pathologies. UPP seems also to be very important in the function of eukaryote cells of the human parasites like Plasmodium falciparum, the causal agent of the neglected disease Malaria. Hence, the UPP could be considered as an attractive target for the development of compounds with Anti-Malarial or Anti-cancer properties. Recent online databases like ChEMBL contains a larger quantity of information in terms of pharmacological assay protocols and compounds tested as UPP inhibitors under many different conditions. This large amount of data give new openings for the computer-aided identification of UPP inhibitors, but the intrinsic data diversity is an obstacle for the development of successful classifiers. To solve this problem here we used the Bob-Jenkins moving average operators and the atom-based quadratic molecular indices calculated with the software TOMOCOMD-CARDD (TC) to develop a quantitative model for the prediction of the multiple outputs in this complex dataset. Our multi-target model can predict results for drugs against 22 molecular or cellular targets of different organisms with accuracies above 70% in both training and validation sets. PMID:26427384
Francisco Nadal-De Simone
The paper estimates two time-varying parameter models of Chilean inflation: a Phillips curve model and a small open economy model. Their out-of-sample forecasts are compared with those of simple Box-Jenkins models. The main findings are; forecasts that include the pre-announced inflation target as a regressor are relatively better; the Phillips curve model outperforms the small open economy model in out-of-sample forecasts; and although Box-Jenkins models outperform the two models for short-t...
One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.
Having in mind that the concept of tourism planning could not be applied if forecasting of tourism demand is neglected, the paper underlines the importance of application of forecasting methods in projection of future tourism trends. In that respect, two quantitative methods were used: (1) the method of exponential smoothing, through two of its variants: Double Exponential Smoothing and the Holt-Winters; (2) the Box-Jenkins methodology, through several alternative specifications. The result o...
Most models for the time series of stock prices have centered on autoregressive (AR) processes. Traditionaly, fundamantal Box-Jenkins analysis  have been the mainstream methodology used to develop time series models. Next, we briefly describe the develop a classical AR model for stock price forecasting. Then a fuzzy regression model is then introduced Following this description, an artificial fuzzy neural network based on B-spline member ship function is presented as an alternative to ...
K. Prabakaran; P.Nadhiya; S.Bharathi; M.Isaivani
Pulses area and production in India data for the period of 1950-51 to 2011-12 were analyzed by time series methods. Auto Correlation Function (ACF) and Partial Auto Correlation Function (PACF) were calculated for the data. Appropriate Box-Jenkins Auto Regressive Integrated Moving Average (ARIMA) model was fitted. Validity of the model was tested using standard statistical techniques.ARIMA (1, 1, 0) and ARIMA (2, 1, 1) model were used to forecast area and production in India fo...
Lim, Cristina Teresa
The study aimed to depict the situation of the coconut industry in the Philippines for the future years applying Autoregressive Integrated Moving Average (ARIMA) method. Data on coconut production, one of the major industrial crops of the country, for the period of 1990 to 2012 were analyzed using time-series methods. Autocorrelation (ACF) and partial autocorrelation functions (PACF) were calculated for the data. Appropriate Box-Jenkins autoregressive moving average model was fitted. Validity of the model was tested using standard statistical techniques. The forecasting power of autoregressive moving average (ARMA) model was used to forecast coconut production for the eight leading years.
Messersmith, A M; Moore, A N; Hoover, L W
A multi-echelon system was designed to generate statistical forecasts of menu-item demand in hospitals from one- through twenty-eight-day intervals prior to patient meal service. The three interdependent echelons were: (1) Forecasting patient census, (2) estimating diet category census, and (3) calculating menu-item demand. Eighteen weeks of supper data were utilized to analyze diet category distribution patterns and menu-item preferences, to test forecasting models, and to evaluate the performance of the forecasting system. A cost function was used to evaluate the efficiency of the mathematical forecasting system and manual technique over a nine-week period. The cost of menu-item forecast errors resulting from the use of adaptive exponential smoothing and Box-Jenkins formulations was approximately 40 per cent less than costs associated with the manual system. PMID:649899
Sitepu, Monika S; Kaewkungwal, Jaranit; Luplerdlop, Nathanej; Soonthornworasiri, Ngamphol; Silawan, Tassanee; Poungsombat, Supawadee; Lawpoolsri, Saranath
This study aimed to describe the temporal patterns of dengue transmission in Jakarta from 2001 to 2010, using data from the national surveillance system. The Box-Jenkins forecasting technique was used to develop a seasonal autoregressive integrated moving average (SARIMA) model for the study period and subsequently applied to forecast DHF incidence in 2011 in Jakarta Utara, Jakarta Pusat, Jakarta Barat, and the municipalities of Jakarta Province. Dengue incidence in 2011, based on the forecasting model was predicted to increase from the previous year. PMID:23691630
Kagnicioglu, Celal Hakan; Mogol, Mune
Accomodation is the main area of tourism industry. In order to determine man, machine and material reguirement, right material must be on the right place on the right time. Man and material requirements are depend on room demand. Main aim of this study is to forecast hotel room demand for a five stars and international chain hotel that has been established in Ankara. In order to forecast the room demand, ARIMA, one of the most popular advanced Box-Jenkins Model, has been used. The reason of s...
Yoo, Wucherl; Sim, Alex
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.
For more than a century, statistical relationships have been recognized between atmospheric conditions at locations separated by thousands of miles, referred to as teleconnections. Some of the recognized teleconnections provide useful information about expected hydrologic conditions, so certain records of atmospheric conditions are quantified and published as hydroclimate indices. Certain hydroclimate indices can serve as strong leading indicators of climate patterns over North America and can be used to make skillful forecasts of seasonal runoff. The methodology described here creates a simple-to-use model that utilizes easily accessed data to make forecasts of April through September runoff months before the runoff season begins. For this project, forecasting models were developed for two snowmelt-driven river systems in Colorado and Wyoming. In addition to the global hydroclimate indices, the methodology uses several local hydrologic variables including the previous year's drought severity, headwater snow water equivalent and the reservoir contents for the major reservoirs in each basin. To improve the skill of the forecasts, logistic regression is used to develop a model that provides the likelihood that a year will fall into the upper, middle or lower tercile of historical flows. Categorical forecasting has two major advantages over modeling of specific flow amounts: (1) with less prediction outcomes models tend to have better predictive skill and (2) categorical models are very useful to clients and agencies with specific flow thresholds that dictate major changes in water resources management. The resulting methodology and functional forecasting model product is highly portable, applicable to many major river systems and easily explained to a non-technical audience.
Brabec, Marek; Paulescu, M.; Badescu, V.
Roč. 111, January (2015), s. 320-331. ISSN 0038-092X R&D Projects: GA MŠk LD12009 Grant ostatní: European Cooperation in Science and Technology(XE) COST ES1002 Institutional support: RVO:67985807 Keywords : solar irradiance * forecasting * tilored statistical models Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 3.469, year: 2014
Full Text Available Recent studies have shown the classification and prediction power of the Neural Networks. It has been demonstrated that a NN can approximate any continuous function. Neural networks have been successfully used for forecasting of financial data series. The classical methods used for time series prediction like Box-Jenkins or ARIMA assumes that there is a linear relationship between inputs and outputs. Neural Networks have the advantage that can approximate nonlinear functions. In this paper we compared the performances of different feed forward and recurrent neural networks and training algorithms for predicting the exchange rate EUR/RON and USD/RON. We used data series with daily exchange rates starting from 2005 until 2013.
National Geographicu ekspeditsiooni, mille koosseisu kuulusid Renan Ozturk, Mark Jenkins, Cory Richards, Emily Harrington ja Kilaree O'Neill, püüdlustest tõusta Kagu-Aasia kõrgeima mäe Hkakabo Razi tippu ning mõõta selle täpset kõrgust GPS-i abil
Andrea Freyer Dugas
Full Text Available BACKGROUND: We developed a practical influenza forecast model based on real-time, geographically focused, and easy to access data, designed to provide individual medical centers with advanced warning of the expected number of influenza cases, thus allowing for sufficient time to implement interventions. Secondly, we evaluated the effects of incorporating a real-time influenza surveillance system, Google Flu Trends, and meteorological and temporal information on forecast accuracy. METHODS: Forecast models designed to predict one week in advance were developed from weekly counts of confirmed influenza cases over seven seasons (2004-2011 divided into seven training and out-of-sample verification sets. Forecasting procedures using classical Box-Jenkins, generalized linear models (GLM, and generalized linear autoregressive moving average (GARMA methods were employed to develop the final model and assess the relative contribution of external variables such as, Google Flu Trends, meteorological data, and temporal information. RESULTS: A GARMA(3,0 forecast model with Negative Binomial distribution integrating Google Flu Trends information provided the most accurate influenza case predictions. The model, on the average, predicts weekly influenza cases during 7 out-of-sample outbreaks within 7 cases for 83% of estimates. Google Flu Trend data was the only source of external information to provide statistically significant forecast improvements over the base model in four of the seven out-of-sample verification sets. Overall, the p-value of adding this external information to the model is 0.0005. The other exogenous variables did not yield a statistically significant improvement in any of the verification sets. CONCLUSIONS: Integer-valued autoregression of influenza cases provides a strong base forecast model, which is enhanced by the addition of Google Flu Trends confirming the predictive capabilities of search query based syndromic surveillance. This
Muhammad H. Lee
Full Text Available Problem statement: Forecasting is very important in many types of organizations since predictions of future events must be incorporated into the decision-making process. In the case of tourism demand, better forecast would help directors and investors make operational, tactical and strategic decisions. Besides that, government bodies need accurate tourism demand forecasts to plan required tourism infrastructures, such as accommodation site planning and transportation development, among other needs. There are many types of forecasting methods. Generally, time series forecasting can be divided into classical method and modern methods. Recent studies show that the newer and more advanced forecasting techniques tend to result in improved forecast accuracy, but no clear evidence shows that any one model can consistently outperform other models in the forecasting competition. Approach: In this study, the performance of forecasting between classical methods (Box-Jenkins methods Seasonal Auto-Regressive Integrated Moving Average (SARIMA, Holt Winters and time series regression and modern methods (fuzzy time series has been compared by using data of tourist arrivals to Bali and Soekarno-Hatta gate in Indonesia as case study. Results: The empirical results show that modern methods give more accurate forecasts compare to classical methods. Chens fuzzy time series method outperforms all the classical methods and others more advance fuzzy time series methods. We also found that the performance of fuzzy time series methods can be improve by using transformed data. Conclusion: It is found that the best method to forecast the tourist arrivals to Bali and Soekarno-Hatta was to be the FTS i.e., method after using data transformation. Although this method known to be the simplest or conventional methods of FTS, yet this result should not be odd since several previous studies also have shown that simple method could outperform more advance or complicated methods.
Niaz Md. FarhatRahman
Full Text Available The present study was carried out to estimate growth pattern and examine the best ARIMA model to efficiently forecasting pigeon pea, chickpea and field pea pulse production in Bangladesh. It appeared that the time series data for pigeon pea, chickpea and field pea were 1st order homogenous stationary. Two types of models namely Box-Jenkins type Autoregressive Integrated Moving Average (ARIMA and deterministic type growth models, are examined to identify the best forecasting models for pigeon pea, chickpea and field pea pulse production in Bangladesh. The study revealed that the best models were ARIMA (1, 1 and 1, ARIMA (0, 1 and 0 and ARIMA (1, 1 and 3 for pigeon pea, chickpea and field pea pulse production, respectively. Among the deterministic type growth models, the cubic model is best for pigeon pea, chickpea and field pea pulse production. The analysis indicated that short-term forecasts were more efficient for ARIMA models compared to the deterministic models. The production uncertainty of pulse could be minimized if production were forecasted well and necessary steps were taken against losses. The findings of this study would be more useful for policy makers, researchers as well as producers in order to forecast future national pulse production more accurately in the short run.
Full Text Available Golf tourism has become one of the rapidly developing tourism types in Turkey, especially in the Belek region. In this study, detailed information about the development of golf tourism in Turkey from past to present was provided and golf tourism demand to Belek region which is a major golf tourism destinastion in the world and Turkey was modeled and forecasted monthly by Box-Jenkins methodology for the May 2013 –December 2014 period. As a measure of golf tourism demand, number of monthly golf games were taken in the study and the monthly number of golf game statistics of January 2001 – April 2013 in the golf establishments in Belek tourism center were used. By producing ex-ante forecasts it is aimed to create a basis for tourism development plans prepared by the management of private and public sector and to provide support for administrators’ monthly planning decisions.
Dugas, Andrea F.; Jalalpour, Mehdi; Gel, Yulia; Levin, Scott; Torcaso, Fred; Igusa, Takeru; Rothman, Richard
Objective We sought to develop a practical influenza forecast model, based on real-time, geographically focused, and easy to access data, to provide individual medical centers with advanced warning of the number of influenza cases, thus allowing sufficient time to implement an intervention. Secondly, we evaluated how the addition of a real-time influenza surveillance system, Google Flu Trends, would impact the forecasting capabilities of this model. Introduction Each year, influenza results in increased Emergency Department crowding which can be mitigated through early detection linked to an appropriate response. Although current surveillance systems, such as Google Flu Trends, yield near real-time influenza surveillance, few demonstrate ability to forecast impending influenza cases. Methods Forecasting models designed to predict one week in advance were developed from weekly counts of confirmed influenza cases over seven seasons (2004 – 2011) divided into training and out-of-sample verification sets. Forecasting procedures using classical Box-Jenkins, generalized linear, and autoregressive methods were employed to develop the final model and assess the relative contribution of external variables such as, Google Flu Trends, meteorological data, and temporal information. Models were developed and evaluated through statistical measures of global deviance and log-likelihood ratio tests. An additional measure of forecast confidence, defined as the percentage of forecast values, during an influenza peak, that are within 7 influenza cases of the actual data, was examined to demonstrate practical utility of the model. Results A generalized autoregressive Poisson (GARMA) forecast model integrating previous influenza cases with Google Flu Trends information provided the most accurate influenza case predictions. Google Flu Trend data was the only source of external information providing significant forecast improvements (p = 0.00002). The final model, a GARMA intercept
Full Text Available En s’appuyant sur des recherches menées auprès d’enseignants d’anglais en Europe, en Asie et en Amérique Latine, Jennifer Jenkins étudie le lien entre les représentations des individus sur les langues et leur apprentissage et leur attitude face à l’enseignement de l’anglais comme langue véhiculaire internationale. C’est l’objet des trois premiers chapitres de l’ouvrage, qui explorent le décalage, d’une part, entre les représentations des individus sur la L2 et la réalité de celle-ci du point ...
En s’appuyant sur des recherches menées auprès d’enseignants d’anglais en Europe, en Asie et en Amérique Latine, Jennifer Jenkins étudie le lien entre les représentations des individus sur les langues et leur apprentissage et leur attitude face à l’enseignement de l’anglais comme langue véhiculaire internationale. C’est l’objet des trois premiers chapitres de l’ouvrage, qui explorent le décalage, d’une part, entre les représentations des individus sur la L2 et la réalité de celle-ci du point ...
Field samples in mango orchards growing in Actopan region of Veracruz, Mexico were taken weekly during a 79-week period to capture adults of two of the main fruit flies pests of mango (Mangifera indica L.) in tropical Mexico: Anastrepha obliqua (Macquart) and A. ludens (Loew) (Diptera: Tephritidae). The data were analysed using Time Series models according to the Box and Jenkins methodology to attempt predicting the behaviour of the pest populations as basic knowledge for integrated pest management. Flies captures were estimated by FTD (flies trap day) method = (number of flies captured x number of traps used)/ number of days of traps deployment). We used McPhail glass traps for the two different species of fruit flies captures during 79 weeks, each one representing the two time series used in this work. After running all the four main steps (identification, parameters estimation, verification and forecasting) indicated in the Box and Jenkins methodology to find the adequate time series models, the ARMA (1,2,2,1) and ARMA (2,1,0,1) models, both with a seasonal behaviour, fully fitted the field observed behavioural pattern for A. obliqua and A. ludens, respectively, and gave good predictions of its future values up to four weeks in advance. This information could help to take the management strategies in an opportune and accurately way when the fruit flies populations surpassing its innocuous level numbers. On the comparative predictions with other different mathematical models, the sixth deg.ree polynomial model was able to made good forecastings in the two series studied
Prass, Taiane S.; Bravo, Juan Martin; Clarke, Robin T.; Collischonn, Walter; Lopes, SíLvia R. C.
The paper compares forecasts of mean monthly water levels up to six months ahead at Ladário, on the Upper Paraguay River, Brazil, estimated from two long-range dependence models. In one of them, the marked seasonal cycle was removed and a fractionally differenced model was fitted to the transformed series. In the other, a seasonal fractionally differenced model was fitted to water levels without transformation. Forecasts from both models for periods up to six months ahead were compared with forecasts given by simpler "short-range dependence" Box-Jenkins models, one fitted to the transformed series, the other a seasonal autoregressive moving average (ARMA) model. Estimates of parameters in the four models (two "long-range dependence", two "short-range dependence") were updated at six-monthly intervals over a 20 year period, and forecasts were compared using root mean square errors (rmse) between water-level forecasts and observed levels. As judged by rmse, performances of the two long-range dependence models, and of the ARMA (1,1) short-range dependence model, were very similar; all three out-performed the seasonal short-range dependence ARMA model. There was evidence that all models performed better during recession periods, than on the hydrograph rising limb.
The analysis of petroleum product demand became a privileged thrust of research following the modifications in terms of structure and level of the petroleum markets since eighties. The greatest importance to econometrics models of Energy demand, joint works about nonstationary data, explained the development of error-correction models and the co-integration. In this context, the short term econometrics modelling of petroleum product demand does not only focus on forecasts but also on the measure of the gain acquired from using error-correction techniques and co-integration. It's filling to take the influence of technical improvement and environment pressures into account in econometrics modelling of petroleum products demand. The first part presents the evolution of Energy Demand in France and more particularly the petroleum product demand since 1986. The objective is to determine the main characteristics of each product, which will help us to analyse and validate the econometrics models. The second part focus on the recent developments in times series modelling. We study the problem of nonstationary data and expose different unit root tests. We examine the main approaches to univariate and multivariate modelling with nonstationary data and distinguish the forecasts of the latter's. The third part is intended to applications; its objective is to illustrate the theoretic developments of the second part with a comparison between the performances of different approaches (approach Box and Jenkins, Johansen approach's and structural approach). The models will be applied to the main French petroleum market. The observed asymmetrical demand behaviour is also considered. (author)
Full Text Available Problem statement: Forecasting of electricity load demand is an essential activity and an important function in power system planning and development. It is a prerequisite to power system expansion planning as the world of electricity is dominated by substantial lead times between decision making and its implementation. The importance of demand forecasting needs to be emphasized at all level as the consequences of under or over forecasting the demand are serious and will affect all stakeholders in the electricity supply industry. Approach: If under estimated, the result is serious since plant installation cannot easily be advanced, this will affect the economy, business, loss of time and image. If over estimated, the financial penalty for excess capacity (i.e., over-estimated and wasting of resources. Therefore this study aimed to develop new forecasting model for forecasting electricity load demand which will minimize the error of forecasting. In this study, we explored the development of rule-based method for forecasting electricity peak load demand. The rule-based system synergized human reasoning style of fuzzy systems through the use of set of rules consisting of IF-THEN approximators with the learning and connectionist structure. Prior to the implementation of rule-based models, SARIMAT model and Regression time series were used. Results: Modification of the basic regression model and modeled it using Box-Jenkins auto regressive error had produced a satisfactory and adequate model with 2.41% forecasting error. With rule-based based forecasting, one can apply forecaster expertise and domain knowledge that is appropriate to the conditions of time series. Conclusion: This study showed a significant improvement in forecast accuracy when compared with the traditional time series model. Good domain knowledge of the experts had contributed to the increase in forecast accuracy. In general, the improvement will depend on the conditions of the data
Full Text Available From the day one, mankind has always been interested in to the future. As the civilization advanced with growing sophistication in all phases of life, the need to look in to the future also grew with it. Today every government, public private organizations, as well as an individual would like to predict and plan for the future. In order to attain a better growth in the economy of a country, modeling and forecasting is the most important tool now a day, this can be done by one of the statistical technique called a Time series analysis. In this paper we tried to build a time series model called ARIMA (Auto Regressive Integrated Moving Average model with particular reference of Box and Jenkins approach on annually total Imports and Exports of Pakistan from the year 1947 to the year 2013 with useful statistical software R. Validity of the fitted model is tested using standard statistical techniques. The fitted model is then use to forecast some future values of Imports and export of Pakistan. It is found that an ARIMA (2, 2, 2 and ARIMA (1, 2, 2 model looks suitable to forecast the annual Imports and Exports of Pakistan respectively. We also found an increasing trend both in case of Imports and Exports during this study.
Smith, J. David; Minda, John Paul; Washburn, David A.
In influential research, R. N. Shepard, C. I. Hovland, and H. M. Jenkins (1961) surveyed humans' categorization abilities using tasks based in rules, exclusive-or (XOR) relations, and exemplar memorization. Humans' performance was poorly predicted by cue-conditioning or stimulus-generalization theories, causing Shepard et al. to describe it in…
Kurtz, Kenneth J.; Levering, Kimery R.; Stanton, Roger D.; Romero, Joshua; Morris, Steven N.
The findings of Shepard, Hovland, and Jenkins (1961) on the relative ease of learning 6 elemental types of 2-way classifications have been deeply influential 2 times over: 1st, as a rebuke to pure stimulus generalization accounts, and again as the leading benchmark for evaluating formal models of human category learning. The litmus test for models…
Full Text Available This paper deals with the combined effect of roughness and slip velocity on the performance of a Jenkins model based ferrofluid squeeze film in curved annular plates. Beavers and Joseph’s slip model has been adopted to incorporate the effect of slip velocity. The stochastic model of Christensen and Tonder has been deployed to evaluate the effect of surface roughness. The associated stochastically averaged Reynolds type equation is solved to derive the pressure distribution, leading to the calculation of load carrying capacity. The graphical representation makes it clear that although, the effect of transverse surface roughness is adverse in general, Jenkins model based ferrofluid lubrication provides some measures in mitigating the adverse effect and this becomes more manifest when the slip parameter is reduced and negatively skewed roughness occurs. Of course, a judicious choice of curvature parameters and variance (-ve add to this positive effect.
Borhan, Nurbaizura; Arsad, Zainudin
One of the major contributing sectors for Malaysia's economic growth is tourism. The number of international tourist arrivals to Malaysia has been showing an upward trend as a result of several programs and promotion introduced by the Malaysian government to attract international tourists to the country. This study attempts to model and to forecast tourism demand for Malaysia by three selected countries: the US, Japan and South Korea. This study utilized monthly time series data for the period from January 1999 to December 2012 and employed the well-known Box-Jenkins seasonal ARIMA modeling procedures. Not surprisingly the results show the number of tourist arrivals from the three countries contain strong seasonal component as the arrivals strongly dependent on the season in the country of origin. The findings of the study also show that the number of tourist arrivals from the US and South Korea will continue to increase in the near future. Meanwhile the arrivals from Japan is forecasted to show a drop in the near future and as such tourism authorities in Malaysia need to enhance the promotional effort to attract more tourists from Japan to visit Malaysia.
Simpson, R.W.; Layton, A.P.
Box-Jenkins (1970) time series models are used to predict peak afternoon O3 levels. Data sets from three monitoring stations in Brisbane, Queensland, Australia, are used in the analysis, one of the stations being inner-city and the others being outer-city. It is found that univariate models using only the peak O3 data-set at a site to predict future peak O3 levels are unsatisfactory. However bivariate models using peak O3 data from one site to predict peak O3 levels at another site yield good results. However it is clear that these results only arise because the O3 is formed in a well mixed layer over the region leading to a high degree of correlation between O3 peaks throughout the region. 15 references.
Patel Jimit R.
Full Text Available This paper analyzes the combined effect of slip velocity and transverse roughness on the performance of a Jenkins model based ferrofluid lubrication of a squeeze film in curved rough annular plates. The slip model of Beavers and Joseph has been invoked to evaluate the effect of slip velocity. In order to find the effect of surface roughness the stochastic averaging model of Christensen and Tonder has been used. The pressure distribution is obtained by solving the concerned stochastically averaged Reynolds type equation. The load carrying capacity is calculated. The graphical representations of the results indicate that the effect of transverse surface roughness is adverse in general, however, the situation is relatively better in the case of negatively skewed roughness. Further, Jenkins model based ferrofluid lubrication offers some measures in reducing the adverse effect of roughness when slip parameter is kept at reduced level with a suitable ratio of curvature parameters. Lastly, the positive effect of magnetization gets a boost due to the combined effect of variance (-ve and negatively skewed roughness suitably choosing the aspect ratio.
Rabi, Rahel; Minda, John Paul
Shepard, Hovland, and Jenkins (1961) examined the categorization abilities of younger adults using tasks involving single-dimensional rule learning, disjunctive rule learning, and family resemblance learning. The current study examined category learning in older adults using this well-known category set. Older adults, like younger adults, found category tasks with a single relevant dimension the easiest to learn. In contrast to younger adults, older adults found complex disjunctive rule-based categories harder to learn than family resemblance based categories. Disjunctive rule-based category learning appeared to be the most difficult for older adults to learn because this category set placed the heaviest demands on working memory, which is known to be a cognitive function that declines with normal aging. The authors discuss why complex rule-based category learning is considered more difficult for older adults to learn relative to younger adults, drawing parallels to developmental research. (PsycINFO Database Record PMID:26765750
Kalnay, Eugenia; Dalcher, Amnon
It is shown that it is possible to predict the skill of numerical weather forecasts - a quantity which is variable from day to day and region to region. This has been accomplished using as predictor the dispersion (measured by the average correlation) between members of an ensemble of forecasts started from five different analyses. The analyses had been previously derived for satellite-data-impact studies and included, in the Northern Hemisphere, moderate perturbations associated with the use of different observing systems. When the Northern Hemisphere was used as a verification region, the prediction of skill was rather poor. This is due to the fact that such a large area usually contains regions with excellent forecasts as well as regions with poor forecasts, and does not allow for discrimination between them. However, when regional verifications were used, the ensemble forecast dispersion provided a very good prediction of the quality of the individual forecasts.
At the 1927 Solvay conference, Einstein presented a thought experiment intended to demonstrate the incompleteness of the quantum mechanical description of reality. In the following years, the thought experiment was picked up and modified by Einstein, de Broglie, and several other commentators into a simple scenario involving the splitting in half of the wave function of a single particle in a box. In this paper we collect together several formulations of this thought experiment from the existing literature; analyze and assess it from the point of view of the Einstein-Bohr debates, the EPR dilemma, and Bell's theorem; and generally lobby for Einstein's Boxes taking its rightful place alongside similar but historically better-known quantum mechanical thought experiments such as EPR and Schroedinger's Cat.
At the 1927 Solvay conference, Einstein presented a thought experiment intended to demonstrate the incompleteness of the quantum mechanical description of reality. In the following years, the thought experiment was picked up and modified by Einstein, de Broglie, and several other commentators into a simple scenario involving the splitting in half of the wave function of a single particle in a box. In this paper we collect together several formulations of this thought experiment from the exist...
Smith, Jason A.; Richman, Gabriel; DeStefano, John; Pryor, James; Rao, Tejas; Strecker-Kellogg, William; Wong, Tony
Centralized configuration management, including the use of automation tools such as Puppet, can greatly increase provisioning speed and efficiency when configuring new systems or making changes to existing systems, reduce duplication of work, and improve automated processes. However, centralized management also brings with it a level of inherent risk: a single change in just one file can quickly be pushed out to thousands of computers and, if that change is not properly and thoroughly tested and contains an error, could result in catastrophic damage to many services, potentially bringing an entire computer facility offline. Change management procedures can—and should—be formalized in order to prevent such accidents. However, like the configuration management process itself, if such procedures are not automated, they can be difficult to enforce strictly. Therefore, to reduce the risk of merging potentially harmful changes into our production Puppet environment, we have created an automated testing system, which includes the Jenkins CI tool, to manage our Puppet testing process. This system includes the proposed changes and runs Puppet on a pool of dozens of RedHat Enterprise Virtualization (RHEV) virtual machines (VMs) that replicate most of our important production services for the purpose of testing. This paper describes our automated test system and how it hooks into our production approval process for automatic acceptance testing. All pending changes that have been pushed to production must pass this validation process before they can be approved and merged into production.
and additive SARIMA models gave more accurate forecasted values at out-sample datasets than multiplicative SARIMA model for airline and tourist arrivals datasets respectively. This study is valuable contribution to the Box-Jenkins procedure particularly at the model identification and estimation steps in SARIMA model. Further work involving multiple seasonal ARIMA models, such as short term load data forecasting in certain countries, may provide further insights regarding the subset, multiplicative or additive orders.
Full Text Available There appeared to be a change in labour productivity growth in Norway (a fall in the growth rate in the middle of the 2000s, followed by a slight recovery at the end of the period under consideration (1971-2011. The 2007-2009 financial and economic crisis in Norway (which resulted from the banking crisis caused an even greater drop in labour productivity growth to the extent that it in 2008 it reached its lowest point over the last three decades. After 2008, labour productivity growth started to increase. In this paper, in order to forecast time-series labour productivity growth in Norway for the period 2012-2021, the ARIMA model is fitted to Norwegian time-series labour productivity growth data obtained in the period 1971-2011. Using the Box-Jenkins model selection methodology, ARIMA (1, 1, 1 with no constant is selected as an appropriate model. As the selected ARIMA model indicates, labour productivity growth in Norway shall continue to increase very slowly and will ultimately reach a non-zero constant in the forecast period (2012-2021 following its recovery after 2008. Long-term forecasts for time-series labour productivity growth in Norway using ARIMA (1, 1, 1 with no constant will also reach a non-zero constant. Initially, it might be concluded that slow technological development as a result of the 2007-2009 financial and economic crisis could explain the slowdown in the recovery of labour productivity growth both in the forecast period (2012-2021 and over the longer term. However, due to the fact that the 2007-2009 financial and economic crisis has changed the underlying process which Norwegian labour productivity growth rate followed in the immediately preceding period, and that a technological revolution, which can be considered as a contributing factor, also took place in that period, it seems unlikely that a single labour productivity growth time series will be rich enough to describe the variation in the data. From the data and the
Nguyen, Dang; Nguyen, Nghia; Nguyen, Duy; Dinh,Tri; Le,Dinh; Duong,Dinh
The Laotian Rock Rat Laonastes aenigmamus Jenkins, Kilpatrick, Robinson & Timmins, 2005 was originally discovered in Lao People's Democratic Republic in 2005. This species has been recognized as the sole surviving member of the otherwise extinct rodent family Diatomyidae. Laonastes aenigmamus was initially reported only in limestone forests of Khammouane Province, Central Lao. A second population was recently discovered in Phong Nha Ke Bang National Park (PNKB NP), Quang Binh Province, Centra...
Mohd Z. Ibrahim; Roziah Zailan; Marzuki Ismail; Muhd S. Lola
Problem statement: In keeping abreast with Malaysias rapid economic development and to meet the nation's aspiration for an improved quality of life, clean-air legislation limiting industrial and automobile emissions was adopted in 1978. Approach: Yet, to this day, air pollution from both sources still poses a problem for the nation. In order to predict the status of future air quality in Malaysia, a Box-Jenkins ARIMA approach was applied to modeling the time series of monthly maximum 1 h carb...
The analysis of petroleum product demand became a privileged thrust of research following the modifications in terms of structure and level of the petroleum markets since eighties. The greatest importance to econometrics models of Energy demand, joint works about nonstationary data, explained the development of error-correction models and the co-integration. In this context, the short term econometrics modelling of petroleum product demand does not only focus on forecasts but also on the measure of the gain acquired from using error-correction techniques and co-integration. It`s filling to take the influence of technical improvement and environment pressures into account in econometrics modelling of petroleum products demand. The first part presents the evolution of Energy Demand in France and more particularly the petroleum product demand since 1986. The objective is to determine the main characteristics of each product, which will help us to analyse and validate the econometrics models. The second part focus on the recent developments in times series modelling. We study the problem of nonstationary data and expose different unit root tests. We examine the main approaches to univariate and multivariate modelling with nonstationary data and distinguish the forecasts of the latter`s. The third part is intended to applications; its objective is to illustrate the theoretic developments of the second part with a comparison between the performances of different approaches (approach Box and Jenkins, Johansen approach`s and structural approach). The models will be applied to the main French petroleum market. The observed asymmetrical demand behaviour is also considered. (author) 153 refs.
Pang, Yang; Opong, Kwaku; Moutinho, Luia; Li, Yun
This paper tackles the problem of financial forecasting by extending methods developed in automation, engineering and computing science. Current methods existing in the literature for firm-level cash flows are first analysed. Then a grey-box modelling method is developed to elevate the performance of cash-flow prediction. Linear panel data modelling is used as a benchmark model. Experiments with out-of-sample tests are used to validate the grey-box approach. Encouragingly, nonlinear grey-box ...
@@ The latest release of "2009 China Luxury Forecast" shows that while the financial crisis is leading a general decline in demand for luxury brands in Europe,America and Japan,the global economic downturn has had limited impact on Chinese luxury consumption and that there is widespread confidence in the future among Chinese luxury consumers.
The latest release of "2009 China Luxury Forecast" shows that while the financial crisis is leading a general decline in demand for luxury brands in Europe,America and Japan,the global economic downturn has had limited impact on Chinese luxury consumption and that there is widespread confidence in the future among Chinese luxury consumers.
Vovk, Vladimir; Takemura, Akimichi; Shafer, Glenn
We consider how to make probability forecasts of binary labels. Our main mathematical result is that for any continuous gambling strategy used for detecting disagreement between the forecasts and the actual labels, there exists a forecasting strategy whose forecasts are ideal as far as this gambling strategy is concerned. A forecasting strategy obtained in this way from a gambling strategy demonstrating a strong law of large numbers is simplified and studied empirically.
... Meeting Calendar Find an ENT Doctor Near You Voice Box (Laryngeal) Cancer Voice Box (Laryngeal) Cancer Patient Health Information News media ... laryngeal cancer can be severe with respect to voice, breathing, or swallowing. It is fundamentally a preventable ...
Keck, Alexander; Raubold, Alexander
This paper develops a set of time series models to provide short-term forecasts (6 to 18 months ahead) of international trade both at the global level and for selected regions. Our results compare favourably to other forecasts, notably by the International Monetary Fund, as measured by standard evaluation measures, such as the root mean square forecast error. In comparison to other models, our approach offers several methodological advantages, inter alia, a focus on import growth as the core ...
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
Ederington, Louis H; Wei Guan
The forecasting ability of the most popular volatility forecasting models is examined and an alternative model developed. Existing models are compared in terms of four attributes: (1) the relative weighting of recent versus older observations, (2) the estimation criterion, (3) the trade‐off in terms of out‐of‐sample forecasting error between simple and complex models, and (4) the emphasis placed on large shocks. As in previous studies, we find that financial markets have longer memories than ...
Cortez, Paulo; Rocha, Miguel
Nowadays, the ability to forecast the future, based only on past data, leads to strategic advantages, which may be the key to success in organizations. Time Series Forecasting (TSF) allows the modeling of complex systems as ``black-boxes'', being a focus of attention in several research arenas such as Operational Research, Statistics or Computer Science. Alternative TSF approaches emerged from the Artificial Intelligence arena, where optimization algorithms inspired on natural selection pr...
Full Text Available Abstract Background During the last decades, dengue viruses have spread throughout the Americas region, with an increase in the number of severe forms of dengue. The surveillance system in Guadeloupe (French West Indies is currently operational for the detection of early outbreaks of dengue. The goal of the study was to improve this surveillance system by assessing a modelling tool to predict the occurrence of dengue epidemics few months ahead and thus to help an efficient dengue control. Methods The Box-Jenkins approach allowed us to fit a Seasonal Autoregressive Integrated Moving Average (SARIMA model of dengue incidence from 2000 to 2006 using clinical suspected cases. Then, this model was used for calculating dengue incidence for the year 2007 compared with observed data, using three different approaches: 1 year-ahead, 3 months-ahead and 1 month-ahead. Finally, we assessed the impact of meteorological variables (rainfall, temperature and relative humidity on the prediction of dengue incidence and outbreaks, incorporating them in the model fitting the best. Results The 3 months-ahead approach was the most appropriate for an effective and operational public health response, and the most accurate (Root Mean Square Error, RMSE = 0.85. Relative humidity at lag-7 weeks, minimum temperature at lag-5 weeks and average temperature at lag-11 weeks were variables the most positively correlated to dengue incidence in Guadeloupe, meanwhile rainfall was not. The predictive power of SARIMA models was enhanced by the inclusion of climatic variables as external regressors to forecast the year 2007. Temperature significantly affected the model for better dengue incidence forecasting (p-value = 0.03 for minimum temperature lag-5, p-value = 0.02 for average temperature lag-11 but not humidity. Minimum temperature at lag-5 weeks was the best climatic variable for predicting dengue outbreaks (RMSE = 0.72. Conclusion Temperature improves dengue outbreaks forecasts
Server pro průběžnou integraci Jenkins CI umožňuje rozšiřovat svou funkcionalitu pomocí zásuvných modulů. Tyto moduly lze programovat v jazycích Java a Ruby. Podpora pro jazyk Python chybí, přestože se jedná o jeden z nejpopulárnějších programovacích jazyků současnosti. Implementovali jsme proto vývojářské nástroje, které umožňují programovat moduly v jazyce Python a tyto nástroje jsme začlenili do projektu Jenkins CI. K nástrojům byla zveřejněna uživatelská dokumentace. Programátoři mohou te...
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...
Pierdzioch, C.; Rulke, J. C.; Stadtmann, G.
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 that fo...... foster incentives to scatter forecasts around a consensus forecast. (C) 2012 Elsevier B.V. All rights reserved....
Stenholt, Rasmus; Madsen, Claus B.
Enabling users to shape 3-D boxes in immersive virtual environments is a non-trivial problem. In this paper, a new family of techniques for creating rectangular boxes of arbitrary position, orientation, and size is presented and evaluated. These new techniques are based solely on position data...
This thesis deals with the generation of probabilistic forecasts in urban hydrology. In particular, we focus on the case of runoff forecasting for real-time control (RTC) on horizons of up to two hours. For the generation of probabilistic on-line runoff forecasts, we apply the stochastic grey-box...
Nguyen, Dang Xuan; Nguyen, Nghia Xuan; Nguyen, Duy Dinh; Dinh, Tri Huy; Le, Dinh Thuc; Dinh, Duong Hai
The Laotian Rock Rat Laonastesaenigmamus Jenkins, Kilpatrick, Robinson & Timmins, 2005 was originally discovered in Lao People's Democratic Republic in 2005. This species has been recognized as the sole surviving member of the otherwise extinct rodent family Diatomyidae. Laonastesaenigmamus was initially reported only in limestone forests of Khammouane Province, Central Lao. A second population was recently discovered in Phong Nha Ke Bang National Park (PNKB NP), Quang Binh Province, Central Vietnam in 2011. The confirmed distribution range of L.aenigmamus in Vietnam is very small, approximately 150 km(2), covering low karst mountains in five communes of Minh Hoa District, Quang Binh Province, at elevations between 250 and 400 m asl. The Laotian Rock Rat inhabits the lower part of steep karst towers with many rock boulders and crevices under tall limestone evergreen forest. They use small rock crevices for their dens. The natural habitat of this species in PNKB NP has been affected by selected timber harvesting, however, a complex 3-4 layer forest structure is retained. The Laotian Rock Rat is omnivorous, feeding on parts (leaves, buds, fruits and roots) of 18 plant species and also some insects (cicada, mantis, grasshopper). The population of this species in PNKB NP is seriously threatened with extinction due to its very restricted distribution, high hunting pressure, and habitat disturbance. Laonastesaenigmamus is listed in the IUCN Red List as endangered and in the Wildlife and Aquatic Red List of Lao, however, this species has not been listed in the Red Data Book or any conservation legislative documents of Vietnam. PMID:25589873
Full Text Available The Laotian Rock Rat Laonastes aenigmamus Jenkins, Kilpatrick, Robinson & Timmins, 2005 was originally discovered in Lao People's Democratic Republic in 2005. This species has been recognized as the sole surviving member of the otherwise extinct rodent family Diatomyidae. Laonastes aenigmamus was initially reported only in limestone forests of Khammouane Province, Central Lao. A second population was recently discovered in Phong Nha Ke Bang National Park (PNKB NP, Quang Binh Province, Central Vietnam in 2011. The confirmed distribution range of L. aenigmamus in Vietnam is very small, approximately 150 km2, covering low karst mountains in five communes of Minh Hoa District, Quang Binh Province, at elevations between 250 and 400 m asl. The Laotian Rock Rat inhabits the lower part of steep karst towers with many rock boulders and crevices under tall limestone evergreen forest. They use small rock crevices for their dens. The natural habitat of this species in PNKB NP has been affected by selected timber harvesting, however, a complex 3-4 layer forest structure is retained. The Laotian Rock Rat is omnivorous, feeding on parts (leaves, buds, fruits and roots of 18 plant species and also some insects (cicada, mantis, grasshopper. The population of this species in PNKB NP is seriously threatened with extinction due to its very restricted distribution, high hunting pressure, and habitat disturbance. Laonastes aenigmamus is listed in the IUCN Red List as endangered and in the Wildlife and Aquatic Red List of Lao, however, this species has not been listed in the Red Data Book or any conservation legislative documents of Vietnam.
Taylor, Kelley R.
This article presents a sample legal battle that illustrates school officials' "reasonable forecasts" of substantial disruption in the school environment. In 2006, two students from a Texas high school came to school carrying purses decorated with images of the Confederate flag. The school district has a zero-tolerance policy for clothing or…
Lutz, Marilyn; Gallucci, Laura
The paper describes the Maine Music Box and examines its potential as a tool for teaching and learning music. Pedagogical concepts are demonstrated using MIDI, Scorch, image and streaming video files.
The larynx, or voice box, is located in the neck and performs several important functions in the body. The larynx is involved in swallowing, breathing, and voice production. Sound is produced when the air which ...
Russell, David W
Robust control mechanisms customarily require knowledge of the system’s describing equations which may be of the high order differential type. In order to produce these equations, mathematical models can often be derived and correlated with measured dynamic behavior. There are two flaws in this approach one is the level of inexactness introduced by linearizations and the other when no model is apparent. Several years ago a new genre of control systems came to light that are much less dependent on differential models such as fuzzy logic and genetic algorithms. Both of these soft computing solutions require quite considerable a priori system knowledge to create a control scheme and sometimes complicated training program before they can be implemented in a real world dynamic system. Michie and Chambers’ BOXES methodology created a black box system that was designed to control a mechanically unstable system with very little a priori system knowledge, linearization or approximation. All the method need...
Moisés Lima de Menezes
Full Text Available El Análisis Espectral Singular (AES es una técnica no paramétrica que permite la descomposición de una serie de tiempo en una componente de señal y otra de ruido. De este modo, AES es una técnica útil para la extracción de la tendencia, la suavización y el filtro una serie de tiempo. En este artículo se investiga el efecto sobre el desempeño los modelos de Holt-Winters y de Box & Jenkins al ser aplicados a una serie de tiempo filtrada por AES. Tres diferentes metodologías son evaluadas con el enfoque de AES: Análisis de Componentes Principales (ACP, análisis de conglomerados y análisis gráfico de vectores singulares. Con el fin de ilustrar y comparar dichas metodologías, en este trabajo también se presentaron los principales resultados de un experimento computacional para el consumo residencial mensual de electricidad en Brasil.
Full Text Available This paper theoretically analyzes the combined effect of slip velocity and surface roughness on the performance of Jenkins model based ferrofluid squeeze film in curved annular plates. The effect of slip velocity has been studied resorting to the slip model of Beavers and Joseph. The stochastically averaging method of Christensen and Tonders has been deployed for studying the effect of surface roughness. The pressure distribution is derived by solving the associated stochastically averaged Reynolds type equation with suitable boundary conditions, leading to the computation of load carrying capacity. The graphical representations reveal that the transverse surface roughness adversely affects the bearing performance. However, Jenkins model based ferrofluid lubrication offers some scopes in minimizing this adverse effect when the slip parameter is kept at minimum. Of course, an appropriate choice of curvature parameters adds to this positive effect in the case of negatively skewed roughness. Moreover, it is established that this type of bearing system supports certain amount of load; even when there is no flow which does not happen in the case of conventional lubricant based bearing system.
Bertsche, David; The ATLAS collaboration; Welch, Steven; Smith, Dale Shane; Che, Siinn; Gan, K.K.; Boyd, George Russell Jr
The opto-box is a custom mini-crate for housing optical modules, which process and transfer optoelectronic data. The system tightly integrates electrical, mechanical, and thermal functionality into a small package of size 35x10x8 cm^3. Special attention was given to ensure proper shielding, grounding, cooling, high reliability, and environmental tolerance. The custom modules, which incorporate Application Specific Integrated Circuits (ASICs), were developed through a cycle of rigorous testing and redesign. In total, fourteen opto-boxes have been installed and loaded with modules on the ATLAS detector. They are currently in operation as part of the LHC run 2 data read-out chain.
Raiser, Lynne; D'Zamko, Mary Elizabeth
Using environmental materials (such as the phone book and placemats from fast food restaurants) can be a motivating way to teach learning disabled students skills and concepts, as shown in an approach to reading, math, science and nutrition, and social studies instruction using a JELL-O brand gelatin box. (CL)
Bragg, Bobby J.; Casey, John E., Jr.
Boxes made of porous, hydrophobic polymers developed to contain aqueous potassium hydroxide electrolyte solutions of zinc/air batteries while allowing air to diffuse in as needed for operation. Used on other types of batteries for in-cabin use in which electrolytes aqueous and from which gases generated during operation must be vented without allowing electrolytes to leak out.
A system for posting objects into closed containers, such as glove boxes, is described in which the bag used, preferably made of plastic, does not have to be fitted and sealed by the operator during each posting operation. (U.K.)
Presents a multicultural project used with fourth-grade students in which they created a three-dimensional totem pole using leftover cereal boxes. Discusses in detail how to create the totem pole. Explains that students learned about Northwest American Indians in class. (CMK)
This magnetic tape contains the FORTRAN source code, sample input data, and sample output data for the Photochemical Box Model (PBM). The PBM is a simple stationary single-cell model with a variable height lid designed to provide volume-integrated hour averages of O3 and other ph...
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...
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...
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...