DA-MSDL maintains predictive performance on drifting multivariate time series by detecting distribution shifts without labels and adapting via prioritized replay and hierarchical fine-tuning.
A survey of deep learning for time series forecasting: Theories, datasets, and state-of-the-art techniques,
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Drift-Aware Online Dynamic Learning for Nonstationary Multivariate Time Series: Application to Sintering Quality Prediction
DA-MSDL maintains predictive performance on drifting multivariate time series by detecting distribution shifts without labels and adapting via prioritized replay and hierarchical fine-tuning.