  In real-world scenarios, time series forecasting often demands timeliness,
making research on model backbones a perennially hot topic. To meet these
performance demands, 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.
