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Advanced Analytics and Learning on Temporal Data : 9th ECML PKDD Workshop, AALTD 2024, Vilnius, Lithuania, September 9–13, 2024, Revised Selected Papers / edited by Vincent Lemaire, Georgiana Ifrim, Anthony Bagnall, Thomas Guyet, Simon Malinowski, Patrick Schäfer, Romain Tavenard

Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Lecture Notes in Artificial Intelligence ; 15433Publisher: Cham : Springer Nature Switzerland, 2025Publisher: Cham : Imprint: Springer, 2025Edition: 1st ed. 2025Description: 1 Online-Ressource(IX, 147 p. 56 illus., 54 illus. in color.)ISBN:
  • 9783031770661
Subject(s): Additional physical formats: 9783031770654 | 9783031770678 | Erscheint auch als: 9783031770654 Druck-Ausgabe | Erscheint auch als: 9783031770678 Druck-AusgabeDDC classification:
  • 006.3 23
DOI: DOI: 10.1007/978-3-031-77066-1Online resources: Summary: Conformal Prediction Techniques for Electricity Price Forecasting -- Multivariate Human Activity Segmentation Systematic Benchmark with ClaSP -- Comparing the Performance of Recurrent Neural Network and Some Well Known Statistical Methods in the Case of Missing Multivariate Time Series Data -- Accurate and Efficient Real World Fall Detection Using Time Series Techniques -- Highly Scalable Time Series Classification for Very Large Datasets -- Classification of Raw MEG/EEG Data with Detach-Rocket Ensemble An Improved ROCKET Algorithm for Multivariate Time Series Analysis -- Change Detection in Multivariate data streams Online Analysis with Kernel QuantTree -- Weighted Average of Human Motion Sequences for Improving Rehabilitation Assessment.Summary: This book constitutes the refereed proceedings of the 9th ECML PKDD workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2024, held in Vilnius, Lithuania, during September 9-13, 2024. The 8 full papers presented here were carefully reviewed and selected from 15 submissions. The papers focus on recent advances in Temporal Data Analysis, Metric Learning, Representation Learning, Unsupervised Feature Extraction, Clustering, and Classification.PPN: PPN: 1913643239Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-SCS | ZDB-2-SXCS | ZDB-2-LNC
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