Semi4TSF: End-to-End Semi-Supervised Contrastive Representation Learning for Time Series Forecasting
Yuhan Wu, Xiyu Meng, Junru Zhang, Yabo Dong, Dongming Lu · IEEE Transactions on Industrial Informatics · 2025
Learning time series representations with sparse labels presents notable challenges. The surge in unsupervised contrastive learning has garnered increasing interest due to its immense advancements in deriving meaningful representations in semi-supervised settings, typically involving a two-stage process: pretraining on large unlabeled data followed by fine-tuning with few labeled samples. However, this approach has inherent drawbacks: poor knowledge transfer, reduced generalizability, and failure to directly utilize unsupervised contrastive loss from pretraining and valuable supervised loss guided by ground truth to impact the downstream tasks. In response, we introduce a novel end-to-end semi-supervised framework, Semi4TSF, for time series forecasting (TSF). It optimizes unsupervised loss on massive unlabeled data and integrates supervised contrastive and forecasting losses on limited labeled data, enabling the model to see other unlabeled embeddings meanwhile learning useful labeled embeddings, improving generalization. The three losses are jointly to refine the encoder and forecaster. Specifically, the unsupervised learning module applies two instance-wise augmentation banks over the entire series to capture long-term dependencies, suggests a learnable Fourier layer, and fuses temporal and frequency information to uncover intricate temporal-frequency correlations through cross-domain interactions to capture nuanced representations. Extensive experiments on five benchmarks demonstrate that Semi4TSF is an effective and superior end-to-end framework that fills the gap in semi-supervised TSF.