Download or read book Short term Forecasting Model for Crude Oil Price Based on Artificial Neural Networks written by Imad Haidar and published by . This book was released on 2008 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This thesis examines the ability of Artificial Neural Networks (ANN) to predict crude oil spot price direction and short-term trends." --Abstract.
Download or read book Forecasting commodity prices using long short term memory neural networks written by Ly, Racine and published by Intl Food Policy Res Inst. This book was released on 2021-02-10 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper applies a recurrent neural network (RNN) method to forecast cotton and oil prices. We show how these new tools from machine learning, particularly Long-Short Term Memory (LSTM) models, complement traditional methods. Our results show that machine learning methods fit reasonably well with the data but do not outperform systematically classical methods such as Autoregressive Integrated Moving Average (ARIMA) or the naïve models in terms of out of sample forecasts. However, averaging the forecasts from the two type of models provide better results compared to either method. Compared to the ARIMA and the LSTM, the Root Mean Squared Error (RMSE) of the average forecast was 0.21 and 21.49 percent lower, respectively, for cotton. For oil, the forecast averaging does not provide improvements in terms of RMSE. We suggest using a forecast averaging method and extending our analysis to a wide range of commodity prices.
Download or read book A Framework for Multivariate Time Series Prediction of Crude Oil Prices Based on Artificial Neural Networks written by Mohammad Isa Irawan and published by . This book was released on 1998 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Forecasting the Term Structure of Crude Oil Futures Prices with Neural Networks written by Jozef Baruník and published by . This book was released on 2015 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: The paper contributes to the rare literature modeling term structure of crude oil markets. We explain term structure of crude oil prices using dynamic Nelson-Siegel model, and propose to forecast them with the generalized regression framework based on neural networks. The newly proposed framework is empirically tested on 24 years of crude oil futures prices covering several important recessions and crisis periods. We find 1-month, 3-month, 6-month and 12-month-ahead forecasts obtained from focused time-delay neural network to be significantly more accurate than forecasts from other benchmark models. The proposed forecasting strategy produces the lowest errors across all times to maturity.
Download or read book Using Artificial Neural Networks to Forecast the Futures Prices of Crude Oil written by Hassan A. Khazem and published by . This book was released on 2008 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Forecasting Accuracy of Crude Oil Futures Prices written by Mr.Manmohan S. Kumar and published by International Monetary Fund. This book was released on 1991-10-01 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper undertakes an investigation into the efficiency of the crude oil futures market and the forecasting accuracy of futures prices. Efficiency of the market is analysed in terms of the expected excess returns to speculation in the futures market. Accuracy of futures prices is compared with that of forecasts using alternative techniques, including time series and econometric models, as well as judgemental forecasts. The paper also explores the predictive power of futures prices by comparing the forecasting accuracy of end-of-month prices with weekly and monthly averages, using a variety of different weighting schemes. Finally, the paper investigates whether the forecasts from using futures prices can be improved by incorporating information from other forecasting techniques.
Download or read book Forecasting Crude Oil Prices written by Hassan Khazem and published by LAP Lambert Academic Publishing. This book was released on 2011-10 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: Crude oil is the commodity de jour and its pricing is of paramount importance to the layperson as well as to any responsible government. However, one of the main challenges facing econometric pricing models is the forecasting accuracy. Historically, linear and non-linear time series models were used. Although, a great success was achieved in that regard, yet there were no definite and universal conclusions drawn. The crude oil forecasting field is still wide open for improvement, especially when applying different forecasting models and alternative techniques. Toward this end, the proposed research implemented Artificial Neural Network models (ANN). The models will forecast the daily crude oil futures prices from 1996 to 2006, listed in NYMEX. Due to the nonlinearity presented by the test results of the crude oil pricing, it is expected that the ANN models will improve forecasting accuracy. An evaluation of the outcomes of the forecasts among different models was done to authenticate that this is undeniably the situation.
Download or read book World Market Price of Oil written by Adalat Muradov and published by Springer. This book was released on 2019-04-10 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book develops new econometric models to analyze and forecast the world market price of oil. The authors construct ARIMA and Trend models to forecast oil prices, taking into consideration outside factors such as political turmoil and solar activity on the price of oil. Incorporating historical and contemporary market trends, the authors are able to make medium and long-term forecasting results. In the first chapter, the authors perform a broad spectrum analysis of the theoretical and methodological challenges of oil price forecasting. In the second chapter, the authors build and test the econometric models needed for the forecasts. The final chapter of the text brings together the conclusions they reached through applying the models to their research. This book will be useful to students in economics, particularly those in upper-level courses on forecasting and econometrics as well as to politicians and policy makers in oil-producing countries, oil importing countries, and relevant international organizations.
Download or read book Artificial Neural Network Models for Forecasting Global Oil Market Volatility written by Saud Al-Fattah and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Energy market volatility affects macroeconomic conditions and can unduly affect the economies of energy-producing countries. Large price swings can be detrimental to both producers and consumers. Market volatility can cause infrastructure and capacity investments to be delayed, employment losses, and inefficient investments. In sum, the growth potential for energy-producing countries is adversely affected. Undoubtedly, greater stability of oil prices can reduce uncertainty in energy markets, for the benefit of consumers and producers alike. Therefore, modeling and forecasting crude oil price volatility is critical in many financial and investment applications. The purpose of this paper to develop new predictive models for describing and forecasting the global oil price volatility using artificial intelligence with artificial neural network (ANN) modeling technology. Applying the novel approach of ANN, two models were successfully developed: one for WTI futures price volatility and the other for WTI spot prices volatility. These models were successfully designed, trained, verified, and tested using historical oil market data. The estimations and predictions from the ANN models closely match the historical data of WTI from January 1994 to April 2012. They appear to capture very well the dynamics and the direction of the oil price volatility. These ANN models developed in this study can be used: as short-term as well as long-term predictive tools for the direction of oil price volatility, to quantitatively examine the effects of various physical and economic factors on future oil market volatility, to understand the effects of different mechanisms for reducing market volatility, and to recommend policy options and programs incorporating mechanisms that can potentially reduce the market volatility. With this improved method for modeling oil price volatility, experts and market analysts will be able to empirically test new approaches to mitigating market volatility. The outcome of this work provides a roadmap for research to improve predictability and accuracy of energy and crude models.
Download or read book Sesame Price Prediction Using Artificial Neural Network written by Endalamaw Gashaw and published by GRIN Verlag. This book was released on 2020-03-23 with total page 69 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master's Thesis from the year 2019 in the subject Computer Science - Miscellaneous, University of Gondar (Atse Tewodros Cumpas), course: Information technology, language: English, abstract: Agricultural price predictions are an integral component of trade and policy analysis. As the prices of agricultural commodities directly influence the real income of farmers and it also affects the national foreign currency generate. Sesame is highly produced in some tropical and subtropical rain forest Ethiopia region. The thesis is to build a model that can predict market prices of sesame commodity. Based on the complexity of sesame price prediction; the predicting models used for crop are linear regression, support vector machine and neural network models to predict a future price. A data have been taken from the ECX website (www.ecx.com.et) in the interval of January 2013 to March 2019. The total numbers of records selected to the experiments are 5,327 daily prices are used for proposed models. The experimental result had evaluated by RMSE, MSE and CC metrics. We follow six phase CRISP-DM process model for sesame price prediction. The process phase are, business understanding, data understanding, data preparation, modeling, evaluating and deployment.
Download or read book An Application of Artificial Neural Networks in Forecasting Future Oil Price Return Volatilities written by Hamed Shafiee Hasanabadi and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Forecasting Jet Fuel Prices Using Artificial Neural Networks written by Mary A. Kasprzak and published by . This book was released on 1995 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial neural networks provide a new approach to commodity forecasting that does not require algorithm or rule development. Neural networks have been deemed successful in applications involving optimization, classification, identification, pattern recognition and time series forecasting. With the advent of user friendly, commercially available software packages that work in a spreadsheet environment, such as Neural Works Predict by NeuralWare, more people can take advantage of the power of artificial neural networks. This thesis provides an introduction to neural networks, and reviews two recent studies of forecasting commodities prices. This study also develops a neural network model using Neural Works Predict that forecasts jet fuel prices for the Defense Fuel Supply Center (DFSC). In addition, the results developed are compared to the output of an econometric regression model, specifically, the Department of Energy's Short-Term Integrated Forecasting System (STWS) model. The Predict artificial neural network model produced more accurate results and reduced the contribution of outliers more effectively than the STIFS model, thus producing a more robust model.
Download or read book A Blending Ensemble Learning Model for Crude Oil Price Prediction written by Mahmudul Hasan and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Given that the price of crude oil is driven by a number of factors with varying frequency, it is difficult to accurately capture its behavior, which in turn leads to challenges in forecasting. Moreover, different mechanisms of fluctuations have been observed at different time series periods. To efficiently capture these diverse fluctuation profiles, we propose to combine heterogenous predictors for predicting the crude oil price. Specifically, a forecasting model is developed using blended ensemble learning is developed that combines various machine learning methods, including linear regression, k-nearest neighbor regression, regression trees, support vector regression, and ridge regression. Brent and WTI crude oil data at various time series frequencies are used to validate the proposed blending ensemble learning approach. To show the effectiveness of the proposed model, its performance is compared with existing individual and ensemble learning methods used for crude oil price prediction, such as lasso regression, bagging lasso regression, boosting, random forest, and support vector regression. We show that our proposed blending ensemble learning model dominates the existing forecasting models in terms of forecasting errors. The proposed model exhibits a good prediction performance for both short- and long-term forecasting horizons, which is beneficial to stakeholders and related industries that depend on this energy source.
Download or read book Using Artificial Neural Networks for Timeseries Smoothing and Forecasting written by Jaromír Vrbka and published by Springer Nature. This book was released on 2021-09-04 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this publication is to identify and apply suitable methods for analysing and predicting the time series of gold prices, together with acquainting the reader with the history and characteristics of the methods and with the time series issues in general. Both statistical and econometric methods, and especially artificial intelligence methods, are used in the case studies. The publication presents both traditional and innovative methods on the theoretical level, always accompanied by a case study, i.e. their specific use in practice. Furthermore, a comprehensive comparative analysis of the individual methods is provided. The book is intended for readers from the ranks of academic staff, students of universities of economics, but also the scientists and practitioners dealing with the time series prediction. From the point of view of practical application, it could provide useful information for speculators and traders on financial markets, especially the commodity markets.
Download or read book Advanced Models of Energy Forecasting written by Xun Zhang and published by Frontiers Media SA. This book was released on 2022-11-23 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Forecasting Jet Fuel Prices Using Artificial Neural Networks written by Mary A. Kasprzak and published by . This book was released on 1995 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial neural networks provide a new approach to commodity forecasting that does not require algorithm or rule development. Neural networks have been deemed successful in applications involving optimization, classification, identification, pattern recognition and time series forecasting. With the advent of user friendly, commercially available software packages that work in a spreadsheet environment, such as Neural Works Predict by NeuralWare, more people can take advantage of the power of artificial neural networks. This thesis provides an introduction to neural networks, and reviews two recent studies of forecasting commodities prices. This study also develops a neural network model using Neural Works Predict that forecasts jet fuel prices for the Defense Fuel Supply Center (DFSC). In addition, the results developed are compared to the output of an econometric regression model, specifically, the Department of Energy's Short-Term Integrated Forecasting System (STWS) model. The Predict artificial neural network model produced more accurate results and reduced the contribution of outliers more effectively than the STIFS model, thus producing a more robust model.
Download or read book Learning Deep Architectures for AI written by Yoshua Bengio and published by Now Publishers Inc. This book was released on 2009 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.
