Minusformer：通过渐进式学习残差改进时间序列预测
Minusformer: Improving Time Series Forecasting by Progressively Learning Residuals
AI总结：
针对时间序列预测模型过拟合问题，提出双流减法与逐层残差学习的 Minusformer，在多数据集上较现有方法平均提升 11.9%。
摘要：
本文发现，广泛使用的时间序列（TS）预测模型容易出现严重过拟合。为解决这一问题，我们采用去冗余方法，逐步恢复未来区间内时间序列的内在价值。具体而言，我们提出一种双流减法机制，这是一种深度 Boosting 集成学习方法。通过将信息聚合机制从加法转向减法，对原始 Transformer 进行改造。随后，我们在原模型的每个模块中加入辅助输出分支，构建一条通向最终预测的高速通道。该分支中后续模块的输出会减去此前已学习的结果，使模型逐层学习监督信号的残差。该设计促进了由学习驱动的输入流与输出流隐式渐进分解，使模型具备更强的通用性、可解释性和抗过拟合能力。由于模型中的所有聚合均为减号，因此被称为 Minusformer。大量实验表明，所提方法优于现有最先进方法，在多个数据集上平均性能提升 11.9%。代码已发布于 https://github.com/Anoise/Minusformer。
英文摘要：
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 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
