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.
Novel multi-flow multi-scale convolutional neural network developed for quality prediction of batch processes to fuse data with different sampling frequencies
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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.