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Why do EMD-based methods improve prediction? A multiscale complexity perspective
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DOI：10.1002/for.2593.png)
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Abstract
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Empirical 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.
Keywords:
empirical mode decomposition
energy forecasting
entropy
multiscale complexity
time series forecasting
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