Pseudo-Label-Triggered Retraining from Forecast Errors for Online Time Series Forecasting

Excerpt

arXiv:2609.39789v1 Announce Type: new Abstract: Real-world time series forecasting systems operate under non-stationary data streams, where forecasting performance may degrade over time. Although retraining can recover the performance, it incurs non-trivial computational and operational costs. Under limited deployment resources, the key challenge is therefore not only how to retrain but also when to retrain. While existing retraining policies often rely on indirect indicators such as drift alarm