Minusformer: Improving Time Series Forecasting by Progressively Learning Residuals
CoRR(2024)
摘要
In this paper, we find that ubiquitous time series (TS) forecasting models
are prone to severe overfitting. To cope with this problem, we embrace a
de-redundancy approach to progressively reinstate the intrinsic values of TS
for future intervals. Specifically, we renovate the vanilla Transformer by
reorienting the information aggregation mechanism from addition to subtraction.
Then, we incorporate an auxiliary output branch into each block of the original
model to construct a highway leading to the ultimate prediction. The output of
subsequent modules in this branch will subtract the previously learned results,
enabling the model to learn the residuals of the supervision signal, layer by
layer. This designing facilitates the learning-driven implicit progressive
decomposition of the input and output streams, empowering the model with
heightened versatility, interpretability, and resilience against overfitting.
Since all aggregations in the model are minus signs, which is called
Minusformer. Extensive experiments demonstrate the proposed method outperform
existing state-of-the-art methods, yielding an average performance improvement
of 11.9
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