期刊
ENTROPY
卷 17, 期 10, 页码 7167-7184出版社
MDPI
DOI: 10.3390/e17107167
关键词
wavelet entropy; wavelet analysis; crude oil forecasting; Autoregressive Moving Average (ARMA) model
资金
- National Science Fund for Distinguished Young Scholars (NSFC) [71025005]
- National Natural Science Foundation of China (NSFC) [71201054, 91224001]
- National Program for Support of Top-Notch Young Professionals
- Strategic Research Grant of City University of Hong Kong [7004574]
- Fundamental Research Funds for the Central Universities in Beijing University of Chemical Technology (BUCT)
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.
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