Time Series Forecasting (TSF) is a crucial task in various domains, yet existing TSF models rely heavily on high-quality data and insufficiently exploit all available data. This paper explores a novel self-supervised approach to re-label time series datasets by inherently constructing candidate datasets. During the optimization of a simple reconstruction network, intermediates are used as pseudo labels in a self-supervised paradigm, improving generalization for any predictor. We introduce the Self-Correction with Adaptive Mask (SCAM), which discards overfitted components and selectively replaces them with pseudo labels generated from reconstructions. Additionally, we incorporate Spectral Norm Regularization (SNR) to further suppress overfitting from a loss landscape perspective. Our experiments on eleven real-world datasets demonstrate that SCAM consistently improves the performance of various backbone models. This work offers a new perspective on constructing datasets and enhancing the generalization of TSF models through self-supervised learning.

本研究针对时间序列预测中对高质量数据的依赖和现有数据利用不足的问题，提出了一种新颖的自监督标注方法。通过构建候选数据集和利用重构网络的中间结果作为伪标签，这一方法在增强预测模型泛化能力的同时消除了过拟合现象。实验结果表明，该方法在多个真实数据集上显著提升了模型性能，为时间序列预测的自监督学习提供了新的视角。

并非所有数据都是好标签：关于时间序列预测的自监督标注