 Forecasting energy market indices with recurrent neural networks: Case study of crude oil price fluctuationsJie Wang and Jun Wang Energy, 2016, vol. 102, issue C, 365-374 Abstract: In an attempt to improve the forecasting accuracy of crude oil price fluctuations, a new neural network architecture is established in this work which combines Multilayer perception and ERNN (Elman recurrent neural networks) with stochastic time effective function. ERNN is a time-varying predictive control system and is developed with the ability to keep memory of recent events in order to predict future output. The stochastic time effective function represents that the recent information has a stronger effect for the investors than the old information. With the established model the empirical research has a good performance in testing the predictive effects on four different time series indices. Compared to other models, the present model is possible to evaluate data from 1990s to today with extreme accuracy and speedy. The applied CID (complexity invariant distance) analysis and multiscale CID analysis, are provided as the new useful measures to evaluate a better predicting ability of the proposed model than other traditional models. Keywords: Forecast; Energy market; Oil price fluctuation; Empirical predictive effect analysis; CID (complexity invariant distance) and MCID (multiscale CID) measures; Random Elman recurrent neural network (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:energy:v:102:y:2016:i:c:p:365-374 DOI: 10.1016/j.energy.2016.02.098 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
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