Advanced Analytics and Learning on Temporal Data | 5th ECML PKDD Workshop, AALTD 2020, Ghent, Belgium, September 18, 2020, Revised Selected Papers

Vincent Lemaire (u. a.)

ISBN 10: 3030657418 / ISBN 13: 9783030657413

Published by Springer, 2020

About this Item

Advanced Analytics and Learning on Temporal Data | 5th ECML PKDD Workshop, AALTD 2020, Ghent, Belgium, September 18, 2020, Revised Selected Papers | Vincent Lemaire (u. a.) | Taschenbuch | Lecture Notes in Computer Science | x | Englisch | 2020 | Springer | EAN 9783030657413 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Seller Inventory # 119294760

Bibliographic Details

• Title: Advanced Analytics and Learning on Temporal Data | 5th ECML PKDD Workshop, AALTD 2020, Ghent, Belgium, September 18, 2020, Revised Selected Papers
• Publisher: Springer
• Publication Date: 2020
• Language: English
• ISBN 10: 3030657418
• ISBN 13: 9783030657413
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 14D millimeters 235 millimeters width by 155 millimeters height by 14 millimeters depth
• Weight: 376 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.