 Multistep-ahead forecasting of coal prices using a hybrid deep learning modelZakaria Alameer, Ahmed Fathalla, Kenli Li, Haiwang Ye and Zhang Jianhua Resources Policy, 2020, vol. 65, issue C Abstract: An accurate forecasting model for the future coal price fluctuations provides critical information and early warning for government and policymakers to provide a stable supply of energy which considered the main concern for China's policymakers. This paper proposes a deep learning model for accurately forecasting monthly coal price fluctuations at different horizons. The proposed LSTM–DNN model combines long-short term memory (LSTM) and deep neural network (DNN). To demonstrate how LSTM–DNN model leads to improvements in predictive accuracy. We compared the results of the LSTM–DNN model to other competitive models, which include multilayer perceptron neural network (MLP) and support vector machine (SVM). Experimental results show the superiority of the hybrid LSTM–DNN model over the other competitive models and its capability to forecast multiple steps accurately and flexible. Therefore, the proposed approach represents an effective and promising technique for the long-term future prediction of coal price fluctuations. Keywords: Coal price fluctuations; Deep learning; DNN; LSTM; Multistep-ahead forecasting (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jrpoli:v:65:y:2020:i:c:s0301420719305240 DOI: 10.1016/j.resourpol.2020.101588 Access Statistics for this article Resources Policy is currently edited by R. G. Eggert More articles in Resources Policy from Elsevier |
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