Advanced Analytics and Learning on Temporal Data (häftad)
Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
233
Utgivningsdatum
2020-12-16
Upplaga
1st ed. 2020
Förlag
Springer Nature Switzerland AG
Medarbetare
Lemaire, Vincent (ed.), Malinowski, Simon (ed.), Ifrim, Georgiana (ed.), Guyet, Thomas (ed.), Tavenard, Romain (ed.), Bagnall, Anthony (ed.)
Illustrationer
67 Illustrations, color; 21 Illustrations, black and white; X, 233 p. 88 illus., 67 illus. in color.
Dimensioner
234 x 156 x 13 mm
Vikt
345 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783030657413
Advanced Analytics and Learning on Temporal Data (häftad)

Advanced Analytics and Learning on Temporal Data

5th ECML PKDD Workshop, AALTD 2020, Ghent, Belgium, September 18, 2020, Revised Selected Papers

Häftad Engelska, 2020-12-16
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This book constitutes the refereed proceedings of the 4th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2019, held in Ghent, Belgium, in September 2020. The 15 full papers presented in this book were carefully reviewed and selected from 29 submissions. The selected papers are devoted to topics such as Temporal Data Clustering; Classification of Univariate and Multivariate Time Series; Early Classification of Temporal Data; Deep Learning and Learning Representations for Temporal Data; Modeling Temporal Dependencies; Advanced Forecasting and Prediction Models; Space-Temporal Statistical Analysis; Functional Data Analysis Methods; Temporal Data Streams; Interpretable Time-Series Analysis Methods; Dimensionality Reduction, Sparsity, Algorithmic Complexity and Big Data Challenge; and Bio-Informatics, Medical, Energy Consumption, Temporal Data.
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Innehållsförteckning

Temporal Data Clustering.- Classification of Univariate and Multivariate Time Series.- Early Classification of Temporal Data.- Deep Learning and Learning Representations for Temporal Data.- Modeling Temporal Dependencies.- Advanced Forecasting and Prediction Models.- Space-Temporal Statistical Analysis.- Functional Data Analysis Methods.- Temporal Data Streams.- Interpretable Time-Series Analysis Methods.- Dimensionality Reduction, Sparsity, Algorithmic Complexity and Big Data Challenge.- Bio-Informatics, Medical, Energy Consumption, Temporal Data.