A multiscale time-series decomposition learning for crude oil price forecasting

Jinghua Tan, Zhixi Li, Chuanhui Zhang, Long Shi and Yuansheng Jiang

Energy Economics, 2024, vol. 136, issue C

Abstract: Crude oil price forecasting is important for market participants and policymakers. However, accurately tracking oil prices is quite a challenging task due to the complexity of temporal oil data and the nonlinear relationships involved in the forecasting task. In this study, a multiscale time-series decomposition learning framework is proposed to deal with this issue. First, a multiscale temporal processing module is designed to capture different frequency time-series patterns in historical data at various scales. Then, a multiscale decomposition technique is applied to decompose historical crude oil data into various temporal modes, involving global shared information across multiple scales, as well as local specific information that varies at each scale. Finally, a multiscale fusion mechanism is employed to combine these information, which are further used as inputs to construct nonlinear and complex predictive models for crude oil prices. A series of experiments conducted on Shanghai crude oil market demonstrate that the proposed approach outperforms several econometric and machine learning models.

Keywords: Price forecasting; Multiscale sequences; Mode decomposition (search for similar items in EconPapers)
JEL-codes: C45 C53 D53 F47 G15 Q47 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:136:y:2024:i:c:s0140988324004419

DOI: 10.1016/j.eneco.2024.107733

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