Titlebook: Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track; European Conference, Gianmarco De Francisci Mor

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978-3-031-43429-7The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track978-3-031-43430-3Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Continually Learning Out-of-Distribution Spatiotemporal Data for?Robust Energy Forecasting accordingly. With online learning, models can be updated incrementally with each new data point, allowing them to learn and adapt in real-time. Continual learning methods offer a powerful solution to address the challenge of catastrophic forgetting in online learning, allowing energy forecasting mo

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Comprehensive Transformer-Based Model Architecture for?Real-World Storm Predictionepresentation and for predicting weather events. Specifically, the representation learning stage employs (1) multiple masked autoencoders (MAE)-based encoders with different scalability degrees for extracting multi-scale image patterns and (2) the Word2vec tool to enact their temporal representation

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Circle Attention: Forecasting Network Traffic by?Learning Interpretable Spatial Relationships from?Ioring time series, allowing our model to focus on the most important time series when making predictions. The circle parameters are learned automatically through back-propagation, with the only signal available being the errors made in the traffic forecasting of each sector. To validate the effectiv