qingsongedu / awesome-ai-for-time-series-papers Goto Github PK

I would like to share our recent work on time series prediction.

Time Evidence Fusion Network (TEFN) is a model that balances performance, accuracy, stability, and interoperability. We propose a novel backbone from the perspective of information fusion. Introducing the Basic Probability Assignment (BPA) Module and the Time Evidence Fusion Network (TEFN), based on evidence theory, allows us to achieve superior performance. On the other hand, the perspective of multi-source information fusion effectively improves the accuracy of forecasting. Due to the fact that BPA is generated by fuzzy theory, TEFN also has considerable interpretability. In real data experiments, the TEFN partially achieved state-of-the-art, with low errors comparable to PatchTST, and operating efficiency surpass performance models such as Dlinear. Meanwhile, TEFN has high robustness and small error fluctuations in the random hyperparameter selection. TEFN is not a model that achieves the ultimate in single aspect, but a model that balances performance, accuracy, stability, and interpretability.

Official implementation of "Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecasting" (TEFN) is available on . Our preprint is available on .