AdaptLSTM: Efficient Adaptive Online Learning for Cloud Workload Forecasting under Distribution Drift
AdaptLSTM is an adaptive online learning method designed to improve workload forecasting accuracy efficiently when data distributions drift. It monitors model errors using a validation-calibrated threshold to trigger selective updates only when necessary, avoiding the high cost of naive online learning while outperforming static models.