Contrastive Learning for Time Series Forecasting: Addressing Anomalies
Published:Dec 12, 2025 12:54
•1 min read
•ArXiv
Analysis
This research explores the application of contrastive learning techniques to improve time series forecasting models, with a specific focus on anomaly detection. The use of contrastive learning could lead to more robust and accurate forecasting in the presence of unusual data points.
Key Takeaways
- •Applies contrastive learning to time series forecasting.
- •Addresses the challenge of anomaly detection within time series data.
- •Likely improves the robustness and accuracy of forecasts.
Reference
“The research focuses on contrastive time series forecasting with anomalies.”