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Advanced Analytics and Learning on Temporal Data [electronic resource] : 5th ECML PKDD Workshop, AALTD 2020, Ghent, Belgium, September 18, 2020, Revised Selected Papers / edited by Vincent Lemaire, Simon Malinowski, Anthony Bagnall, Thomas Guyet, Romain Tavenard, Georgiana Ifrim.

Contributor(s): Lemaire, Vincent [editor.] | Malinowski, Simon [editor.] | Bagnall, Anthony [editor.] | Guyet, Thomas [editor.] | Tavenard, Romain [editor.] | Ifrim, Georgiana [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Artificial Intelligence: 12588Publisher: Cham : Springer International Publishing : Imprint: Springer, 2020Edition: 1st ed. 2020.Description: X, 233 p. 88 illus., 67 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783030657420.Subject(s): Artificial intelligence | Data mining | Social sciences -- Data processing | Machine learning | Education -- Data processing | Computer networks  | Artificial Intelligence | Data Mining and Knowledge Discovery | Computer Application in Social and Behavioral Sciences | Machine Learning | Computers and Education | Computer Communication NetworksAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
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 eBookSummary: 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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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.

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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