Contrastive Time Series Forecasting with Anomalies

TMLR Paper8859 Authors

Abstract:

Time-series forecasting predicts future values from past data. In real-world settings, some
anomalous events have lasting effects and influence the forecast, while others are short-lived
and should be ignored. Standard forecasting models fail to make this distinction, often
either overreacting to noise or missing persistent shifts. We propose Co-TSFA (Contrastive
Time-Series Forecasting with Anomalies), a regularization framework that learns when to
ignore anomalies and when to respond. Co-TSFA generates input-only and input–output
augmentations to model forecast-irrelevant and forecast-relevant anomalies, and introduces
a latent–output alignment loss that ties representation changes to forecast changes. This
encourages invariance to irrelevant perturbations while preserving sensitivity to meaningful
distributional shifts. Experiments on Traffic and Electricity benchmarks, as well as on a
real-world cash-demand dataset, demonstrate that Co-TSFA improves performance under
anomalous conditions while maintaining accuracy on normal data. An anonymized GitHub
repository with the implementation of Co-TSFA is provided here and will be made public
upon acceptance.

Submission Type: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Han-Jia_Ye1

Submission Number: 8859