Multi-Resolution Expansion of Analysis in Time-Frequency Domain for Time Series Forecasting

Time series forecasting plays a crucial role in various real-world applications, such as finance, energy, traffic, and healthcare, providing valuable insights for decision-making processes. The aggregation of information windows with different resolutions has proven effective in time series forecasting tasks and provides the model diverse contextual information. As a result, the network can better capture and model the heterogeneity present in the data, thereby improving performance. However, most of the current work focuses on extracting multilevel-resolution information without considering the possibility that important information can be supplemented. Meanwhile, these methods also tend to ignore the effect of resolution on frequency. To address these challenges, we introduce the Time-Frequency Domain Multi-Resolution Expansion Network (TFMRN) for long-series forecasting using multi-resolution time-frequency data. The proposed TFMRN aims to expand the data in both the time and frequency domains, enabling the model to capture finer details that may not be evident in the original data. In addition, we also propose an Information Gating Unit (IGU) to enhance the selection and guidance of rich information from the expanded time-frequency multi-resolution data. Experimental results demonstrate that the proposed method yields better performance compared with the state-of-the-art methods in both univariate and multivariate time forecasting tasks. Codes are available at
https://github.com/CurtainKevin/TFMRN-master
.