Wavelet Entropy Based Analysis and Forecasting of Crude Oil Price DynamicsReport as inadecuate




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1

School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China

2

College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China





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Academic Editor: Raúl Alcaraz Martínez

Abstract For the modeling of complex and nonlinear crude oil price dynamics and movement, wavelet analysis can decompose the time series and produce multiple economically meaningful decomposition structures based on different assumptions of wavelet families and decomposition scale. However, the determination of the optimal model specification will critically affect the forecasting accuracy. In this paper, we propose a new wavelet entropy based approach to identify the optimal model specification and construct the effective wavelet entropy based forecasting models. The wavelet entropy algorithm is introduced to determine the optimal wavelet families and decomposition scale, that will produce the improved forecasting performance. Empirical studies conducted in the crude oil markets show that the proposed algorithm outperforms the benchmark model, in terms of conventional performance evaluation criteria for the model forecasting accuracy. View Full-Text

Keywords: wavelet entropy; wavelet analysis; crude oil forecasting; Autoregressive Moving Average ARMA model wavelet entropy; wavelet analysis; crude oil forecasting; Autoregressive Moving Average ARMA model





Author: Yingchao Zou 1,2, Lean Yu 1 and Kaijian He 1,*

Source: http://mdpi.com/



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