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This research addresses a key inefficiency in Transformer-based time series forecasting by introducing a strategy to filter out redundant token dependencies, thereby improving model generalization. By jointly optimizing attention entropy and prediction error constraints, the method enables models to focus on fewer but more critical inter-token relationships without sacrificing accuracy. The approach is broadly applicable across various existing Transformer architectures, offering a practical path toward leaner and more robust forecasting systems.
Fewer yet critical: Reducing Redundant Token Dependencies for Transformer-based Time Series Forecasting
arXiv:2503.06867v2 Announce Type: replace-cross Abstract: Time series forecasting (TSF) is important in real-world applications. Recently, Transformer-based methods have achieved strong performance by modeling token dependencies through attention mechanisms. However, existing methods are usually trained mainly with prediction error losses, which may cause models to exploit both critical and redundant token dependencies. Such redundant dependencies can introduce irrelevant information and weaken generalization. To address this issue, we propose a simple yet effective token dependency selection strategy. Specifically, by jointly introducing the attention entropy constraint and prediction error constraint, the model can identify fewer but more critical inter-token dependencies and perform forecasting based on them, thus avoiding the interference of redundant dependencies. The proposed method can be easily extended to various Transformer-based TSF models. Experiments on multiple TSF datasets demonstrate its effectiveness.
Abstract:Time series forecasting (TSF) is important in real-world applications. Recently, Transformer-based methods have achieved strong performance by modeling token dependencies through attention mechanisms. However, existing methods are usually trained mainly with prediction error losses, which may cause models to exploit both critical and redundant token dependencies. Such redundant dependencies can introduce irrelevant information and weaken generalization. To address this issue, we propose a simple yet effective token dependency selection strategy. Specifically, by jointly introducing the attention entropy constraint and prediction error constraint, the model can identify fewer but more critical inter-token dependencies and perform forecasting based on them, thus avoiding the interference of redundant dependencies. The proposed method can be easily extended to various Transformer-based TSF models. Experiments on multiple TSF datasets demonstrate its effectiveness.
Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2503.06867 [cs.AI]
(or arXiv:2503.06867v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2503.06867 Focus to learn more arXiv-issued DOI via DataCite
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