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.

中文翻译：

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原油价格预测的多尺度时间序列分解学习


原油价格预测对于市场参与者和政策制定者来说非常重要。然而，由于时间石油数据的复杂性和预测任务中涉及的非线性关系，准确跟踪石油价格是一项相当具有挑战性的任务。在本研究中，提出了一种多尺度时间序列分解学习框架来解决这个问题。首先，设计多尺度时间处理模块来捕获不同尺度历史数据中的不同频率时间序列模式。然后，应用多尺度分解技术将历史原油数据分解为各种时间模式，涉及跨多个尺度的全局共享信息，以及在每个尺度上变化的局部特定信息。最后，采用多尺度融合机制将这些信息组合起来，进一步用作构建非线性复杂的原油价格预测模型的输入。在上海原油市场进行的一系列实验表明，该方法优于多种计量经济学和机器学习模型。