NeurIPS 2025

Not All Data are Good Labels:
On the Self-supervised Labeling for Time Series
Forecasting
Yuxuan Yang1,2, Dalin Zhang3, Yuxuan Liang4, Hua Lu5, Gang Chen1, 2, Huan Li1,2 B
1The State Key Laboratory of Blockchain and Data Security, Zhejiang University
2Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security
3Space Information Research Institute, Hangzhou Dianzi University
4The Hong Kong University of Science and Technology (Guangzhou)
5Department of Computer Science, Aalborg University, Denmark
{yangyuxuan, cg, lihuan.cs}@zju.edu.cn
zhangdalin@hdu.edu.cn, yuxliang@outlook.com, luhua@cs.aau.dk
Abstract
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 sup-
press 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. The code is available at https://github.com/SuDIS-ZJU/SCAM.
1
Introduction
Time Series Forecasting (TSF) is a crucial task with extensive applications in energy, finance,
engineering, and many other domains. Recent advances in deep learning have resulted in TSF
methods that outperform traditional metho...