UID:
almahu_9948621899502882
Umfang:
X, 233 p. 88 illus., 67 illus. in color.
,
online resource.
Ausgabe:
1st ed. 2020.
ISBN:
9783030657420
Serie:
Lecture Notes in Artificial Intelligence ; 12588
Inhalt:
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.
Anmerkung:
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.
In:
Springer Nature eBook
Weitere Ausg.:
Printed edition: ISBN 9783030657413
Weitere Ausg.:
Printed edition: ISBN 9783030657437
Sprache:
Englisch
DOI:
10.1007/978-3-030-65742-0
URL:
https://doi.org/10.1007/978-3-030-65742-0
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