PCFNet: Enhancing Time Series Forecasting Through Preserving Constant Frequency

Long-term time series forecasting has been widely applied in finance, traffic, and other domains. The stable periodic patterns serve as the foundation for conducting long-term forecasting. However, real-world time series often consist of multi-periodic components and trend components, which poses a significant challenge to time series prediction. In this paper, we introduce PCFNet, a simple yet effective time series forecasting model, which enhances time series forecasting by preserving the constant frequency components that represent the multi-periodicity of time series during the forecasting process. Specifically, PCFNet adaptively identifies the constant frequency components through a simple gated network. Then, the residual frequency components are predicted via a single layer of complex-valued linear layer. Finally, the residual frequency components are added to the constant frequency components to obtain the final outcome. Extensive experimental results across multiple real-world time series datasets demonstrate that PCFNet achieves state-of-the-art performance as a simple architecture.

Authors

• Wenjun Yu
• Wen Li
• Wentao Gao
• Wangyu Wu
• Shouguo Du
• Jiyanglin Li

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