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Book Oil Price Forecasting Using Gene Expression Programming and Artificial Neural Networks

Download or read book Oil Price Forecasting Using Gene Expression Programming and Artificial Neural Networks written by Mohamed M. Mostafa and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This study aims to forecast oil prices using evolutionary techniques such as gene expression programming (GEP) and artificial neural network (NN) models to predict oil prices over the period from January 2, 1986 to June 12, 2012. Autoregressive integrated moving average (ARIMA) models are employed to benchmark evolutionary models. The results reveal that the GEP technique outperforms traditional statistical techniques in predicting oil prices. Further, the GEP model outperforms the NN and the ARIMA models in terms of the mean squared error, the root mean squared error and the mean absolute error. Finally, the GEP model also has the highest explanatory power as measured by the R-squared statistic. The results of this study have important implications for both theory and practice.

Book Forecasting commodity prices using long short term memory neural networks

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.

Book Intelligent Optimization Modelling in Energy Forecasting

Download or read book Intelligent Optimization Modelling in Energy Forecasting written by Wei-Chiang Hong and published by MDPI. This book was released on 2020-04-01 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accurate energy forecasting is important to facilitate the decision-making process in order to achieve higher efficiency and reliability in power system operation and security, economic energy use, contingency scheduling, the planning and maintenance of energy supply systems, and so on. In recent decades, many energy forecasting models have been continuously proposed to improve forecasting accuracy, including traditional statistical models (e.g., ARIMA, SARIMA, ARMAX, multi-variate regression, exponential smoothing models, Kalman filtering, Bayesian estimation models, etc.) and artificial intelligence models (e.g., artificial neural networks (ANNs), knowledge-based expert systems, evolutionary computation models, support vector regression, etc.). Recently, due to the great development of optimization modeling methods (e.g., quadratic programming method, differential empirical mode method, evolutionary algorithms, meta-heuristic algorithms, etc.) and intelligent computing mechanisms (e.g., quantum computing, chaotic mapping, cloud mapping, seasonal mechanism, etc.), many novel hybrid models or models combined with the above-mentioned intelligent-optimization-based models have also been proposed to achieve satisfactory forecasting accuracy levels. It is important to explore the tendency and development of intelligent-optimization-based modeling methodologies and to enrich their practical performances, particularly for marine renewable energy forecasting.

Book Sesame Price Prediction Using Artificial Neural Network

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.

Book Renminbi Exchange Rate Forecasting

Download or read book Renminbi Exchange Rate Forecasting written by Yunjie Wei and published by Routledge. This book was released on 2021-05-10 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the internationalization of Renminbi (RMB), the gradual liberalization of China's capital account and the recent reform of the RMB pricing mechanism, the RMB exchange rate has been volatile. This book examines how we can forecast exchange rate reliably. It explains how we can do so through a new methodology for exchange rate forecasting. The book also analyzes the dynamic relationship between exchange rate and the exchange rate data decomposition and integration, the domestic economic situation, the international economic situation and the public’s expectations and how these interactions would affect the exchange rate. The book also explains why this comprehensive integrated approach is the best model for optimizing accuracy in exchange rate forecasting.

Book Handbook of Big Data Research Methods

Download or read book Handbook of Big Data Research Methods written by Shahriar Akter and published by Edward Elgar Publishing. This book was released on 2023-06-01 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: This state-of-the-art Handbook provides an overview of the role of big data analytics in various areas of business and commerce, including accounting, finance, marketing, human resources, operations management, fashion retailing, information systems, and social media. It provides innovative ways of overcoming the challenges of big data research and proposes new directions for further research using descriptive, diagnostic, predictive, and prescriptive analytics.

Book Machine Learning Optimization and Data Science

Download or read book Machine Learning Optimization and Data Science written by Giuseppe Nicosia and published by Springer Nature. This book was released on 2021-01-06 with total page 701 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set, LNCS 12565 and 12566, constitutes the refereed proceedings of the 6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020, held in Siena, Italy, in July 2020. The total of 116 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 209 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications.

Book Advances in Computing and Data Sciences

Download or read book Advances in Computing and Data Sciences written by Mayank Singh and published by Springer. This book was released on 2019-07-18 with total page 752 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set (CCIS 1045 and CCIS 1046) constitutes the refereed proceedings of the Third International Conference on Advances in Computing and Data Sciences, ICACDS 2019, held in Ghaziabad, India, in April 2019. The 112 full papers were carefully reviewed and selected from 621 submissions. The papers are centered around topics like advanced computing, data sciences, distributed systems organizing principles, development frameworks and environments, software verification and validation, computational complexity and cryptography, machine learning theory, database theory, probabilistic representations.

Book Artificial Neural Network Models for Forecasting Global Oil Market Volatility

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.

Book Short term Forecasting Model for Crude Oil Price Based on Artificial Neural Networks

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.

Book The Air Transportation Industry

Download or read book The Air Transportation Industry written by Rosario Macario and published by Elsevier. This book was released on 2021-11-16 with total page 476 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aviation sector consists of various actors such as airlines, ground handling companies, and others all with conflicting priorities. In order to understand how these actors position themselves in an increasingly competitive market, The Air Transportation Industry: Economic Conflict and Competition analyzes all the market segments in detail, examining such issues as which industrial economic structure drives decisions, the main economic problems, the consequences for negotiations between different actors, impacts on the global aviation market, and much more. This book covers the entire aviation sector including strategies, regulation, resilience, privatization, airport slot management, and more. It examines how economic and strategic struggles underlie the current market structure, both for aviation as a whole and for the constituent actors as carriers, authorities, and handlers. It examines the ways market and nonmarket approaches impact the competitiveness of the air transport industry, offering a complete mapping of the economic actions between actors of the air transport industry. This volume will help readers gain insight into the possible strategic choices and the mutual competitive strength within the future aviation market. Contains contributions from well-known aviation scholars Includes numerous cases studies throughout that explore a wide range of topics Focuses on applied knowledge, with clearly structured chapters examining topics from a global perspective Addresses the ongoing consequences of COVID-19 on the air transportation industry, examining potential strategic responses in the event of subsequent pandemics

Book Forecasting the Term Structure of Crude Oil Futures Prices with Neural Networks

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.

Book What Can Be Learned from the Historical Trend of Crude Oil Prices An Ensemble Approach to Crude Oil Price Forecasting

Download or read book What Can Be Learned from the Historical Trend of Crude Oil Prices An Ensemble Approach to Crude Oil Price Forecasting written by Mingchen Li and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Crude oil price series are nonlinear and highly volatile, making it difficult to obtain satisfactory performance for traditional statistical-based forecasting methods. To improve prediction accuracy, this study proposes a novel learning paradigm by integrating the trajectory similarity method with machine learning models based on the decomposition-ensemble framework. In the proposed learning paradigm, raw data of international crude oil prices are first decomposed using variational mode decomposition (VMD), after which, using sample entropy (SE), the resulting essential modal functions are divided into high and low frequencies. The process aims to reorganize the data by using the forecasting properties of different models. Finally, to obtain the final forecasting results, two models, i.e., the trajectory similarity method (TS) and artificial neural networks (ANN), are applied to predict and sum up the low and high-frequency subseries, respectively. As sample data for validation, this study selected the international crude oil price series of West Texas Intermediate (WTI) and Brent. Experimental results showed that the proposed VMD-SE-TS/ANN learning paradigm significantly outperforms all other benchmark models, including the single models without decomposition and the hybrid models with decomposition. The proposed approach performs best in different evaluation metrics and statistical tests under different horizons, indicating that the proposed VMD-SE-TS/ANN learning paradigm is effective and robust in crude oil price forecasting.