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