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Author: Ly, Racine
Publisher: Intl Food Policy Res Inst
ISBN:
Category : Political Science
Languages : en
Pages : 26
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Book Description
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.
Author: Ly, Racine
Publisher: Intl Food Policy Res Inst
ISBN:
Category : Political Science
Languages : en
Pages : 26
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Book Description
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.
Author: Tamer Shahwan
Publisher:
ISBN: 9783866641174
Category :
Languages : en
Pages : 135
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Book Description
Author: Endalamaw Gashaw
Publisher: GRIN Verlag
ISBN: 3346135187
Category : Computers
Languages : en
Pages : 69
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Book Description
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.
Author: Mary A. Kasprzak
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
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.
Author: Racine Ly
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Author: Jaromír Vrbka
Publisher: Springer Nature
ISBN: 3030756491
Category : Technology & Engineering
Languages : en
Pages : 197
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Book Description
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.
Author: Paul P. Wang
Publisher: Springer Science & Business Media
ISBN: 354072821X
Category : Computers
Languages : en
Pages : 232
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Book Description
Readers will find, in this highly relevant and groundbreaking book, research ranging from applications in financial markets and business administration to various economics problems. Not only are empirical studies utilizing various CI algorithms presented, but so also are theoretical models based on computational methods. In addition to direct applications of computational intelligence, readers can also observe how these methods are combined with conventional analytical methods such as statistical and econometric models to yield preferred results.
Author: Mary A. Kasprzak
Publisher:
ISBN:
Category :
Languages : en
Pages : 50
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Book Description
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.
Author: Walter C. Labys
Publisher: Routledge
ISBN: 1351917080
Category : Business & Economics
Languages : en
Pages : 247
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Book Description
Recent economic growth in China and other Asian countries has led to increased commodity demand which has caused price rises and accompanying price fluctuations not only for crude oil but also for the many other raw materials. Such trends mean that world commodity markets are once again under intense scrutiny. This book provides new insights into the modeling and forecasting of primary commodity prices by featuring comprehensive applications of the most recent methods of statistical time series analysis. The latter utilize econometric methods concerned with structural breaks, unobserved components, chaotic discovery, long memory, heteroskedasticity, wavelet estimation and fractional integration. Relevant tests employed include neural networks, correlation dimensions, Lyapunov exponents, fractional integration and rescaled range. The price forecasting involves structural time series trend plus cycle and cyclical trend models. Practical applications focus on the price behaviour of more than twenty international commodity markets.
Author: Nowrouz Kohzadi
Publisher:
ISBN:
Category :
Languages : en
Pages : 146
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Book Description
