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Explainable online ensemble of deep neural network pruning for time series forecasting., , and . Mach. Learn., 111 (9): 3459-3487 (2022)BRIGHT - Drift-Aware Demand Predictions for Taxi Networks., , , , , and . IEEE Trans. Knowl. Data Eng., 32 (2): 234-245 (2020)Tutorial: Interactive Adaptive Learning., , , and . IAL@PKDD/ECML, volume 3470 of CEUR Workshop Proceedings, page 1-8. CEUR-WS.org, (2023)Simulation and sensor data fusion for machine learning application., , , , , and . Adv. Eng. Informatics, (2022)Active Sampling for Learning Interpretable Surrogate Machine Learning Models., and . DSAA, page 264-272. IEEE, (2020)An Actor-Critic Ensemble Aggregation Model for Time-Series Forecasting., , and . ICDE, page 2255-2260. IEEE, (2021)Explainable Adaptive Tree-based Model Selection for Time Series Forecasting., and . CoRR, (2024)Online Adaptive Multivariate Time Series Forecasting., , and . ECML/PKDD (6), volume 13718 of Lecture Notes in Computer Science, page 19-35. Springer, (2022)A Drift-Based Dynamic Ensemble Members Selection Using Clustering for Time Series Forecasting., , and . ECML/PKDD (1), volume 11906 of Lecture Notes in Computer Science, page 678-694. Springer, (2019)AutoXPCR: Automated Multi-Objective Model Selection for Time Series Forecasting., and . CoRR, (2023)