  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 introduce a dual-stream and subtraction
mechanism, which is a deep Boosting ensemble learning method. And the vanilla
Transformer is renovated 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% across various datasets.The code has
been released at https://github.com/Anoise/Minusformer.
