Why do EMD‐based methods improve prediction? A multiscale complexity perspective

Journal of Forecasting - Tập 38 Số 7 - Trang 714-731 - 2019
Jichang Dong1, Wei Dai2,1, Ling Tang3, Lean Yu4
1School of Economics and Management, University of Chinese Academy of Sciences, Beijing, China
2JD Digital, Beijing, China
3School of Economics and Management, Beihang University, Beijing, China
4School of Economics and Management, Beijing University of Chemical Technology, Beijing, China

Tóm tắt

AbstractEmpirical mode decomposition (EMD)‐based ensemble methods have become increasingly popular in the research field of forecasting, substantially enhancing prediction accuracy. The key factor in this type of method is the multiscale decomposition that immensely mitigates modeling complexity. Accordingly, this study probes this factor and makes further innovations from a new perspective of multiscale complexity. In particular, this study quantitatively investigates the relationship between the decomposition performance and prediction accuracy, thereby developing (1) a novel multiscale complexity measurement (for evaluating multiscale decomposition), (2) a novel optimized EMD (OEMD) (considering multiscale complexity), and (3) a novel OEMD‐based forecasting methodology (using the proposed OEMD in multiscale analysis). With crude oil and natural gas prices as samples, the empirical study statistically indicates that the forecasting capability of EMD‐based methods is highly reliant on the decomposition performance; accordingly, the proposed OEMD‐based methods considering multiscale complexity significantly outperform the benchmarks based on typical EMDs in prediction accuracy.

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Thông tin
Thông tin xuất bản

Journal of Forecasting

Tập 38 Số 7

714-731

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