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This book constitutes the refereed proceedings of the 6th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2021, held during September 13-17, 2021. The workshop was planned to take place in Bilbao, Spain, but was held virtually due to the COVID-19 pandemic.
The 12 full papers presented in this book were carefully reviewed and selected from 21 submissions. They focus on the following topics: Temporal Data Clustering; Classification of Univariate and Multivariate Time Series; Multivariate Time Series Co-clustering; Efficient Event Detection; Modeling Temporal Dependencies; Advanced Forecasting and Prediction Models; Cluster-based Forecasting; Explanation Methods for Time Series Classification; Multimodal Meta-Learning for Time Series Regression; and Multivariate Time Series Anomaly Detection.
Oral Presentation.- Ranking by Aggregating Referees: Evaluating the Informativeness of Explanation Methods for Time Series Classification.- State Space approximation of Gaussian Processes for time-series forecasting.- Fast Channel Selection for Scalable Multivariate Time Series Classification.- Temporal phenotyping for characterisation of hospital care pathways of COVID patients.- A New Multivariate Time Series Co-clustering Non-Parametric Model Applied to Driving-Assistance Systems Validation.- TRAMESINO: Trainable Memory System for Intelligent Optimization of Road Traffic Control.- Detection of critical events in renewable energy production time series.- Poster Presentation.- Multimodal Meta-Learning for Time Series Regression.- Cluster-based Forecasting for Intermittent and Non-intermittent Time Series.- State discovery and prediction from multivariate sensor data.- RevDet: Robust and Memory Efficient Event Detection and Tracking in Large News Feeds.- From Univariate to Multivariate Time Series Anomaly Detection with Non-Local Information.
Titel: | Advanced Analytics and Learning on Temporal Data |
Untertitel: | 6th ECML PKDD Workshop, AALTD 2021, Bilbao, Spain, September 13, 2021, Revised Selected Papers |
Editor: | |
EAN: | 9783030914448 |
ISBN: | 3030914445 |
Format: | Kartonierter Einband |
Herausgeber: | Springer International Publishing |
Anzahl Seiten: | 208 |
Gewicht: | 324g |
Größe: | H235mm x B155mm x T11mm |
Jahr: | 2021 |
Untertitel: | Englisch |
Auflage: | 1st ed. 2021 |
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