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.

翻译：时间序列预测（TSF）是各领域的关键任务，然而现有TSF模型严重依赖高质量数据且未能充分利用所有可用数据。本文探索了一种新颖的自监督方法，通过内在构建候选数据集来重新标注时间序列数据。在优化简单重构网络的过程中，中间结果被用作自监督范式中的伪标签，从而提升任意预测器的泛化能力。我们提出了自适应掩码自校正方法（SCAM），该方法摒弃过拟合成分并选择性地用重构生成的伪标签进行替换。此外，我们从损失景观角度引入谱范数正则化（SNR）以进一步抑制过拟合。在十一个真实数据集上的实验表明，SCAM能持续提升各类骨干模型的性能。本研究为通过自监督学习构建数据集并增强TSF模型泛化能力提供了新的视角。