Advanced Analytics and Learning on Temporal Data (Paperback)

Vincent Lemaire

ISBN 10: 3030914445 / ISBN 13: 9783030914448

Published by Springer Nature Switzerland AG, Cham, 2021

About this Item

Paperback. This book constitutes the refereed proceedings of the 6th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2021, held during September 13-17, 2021. The workshop was planned to take place in Bilbao, Spain, but was held virtually due to the COVID-19 pandemic. The 12 full papers presented in this book were carefully reviewed and selected from 21 submissions. They focus on the following topics: Temporal Data Clustering; Classification of Univariate and Multivariate Time Series; Multivariate Time Series Co-clustering; Efficient Event Detection; Modeling Temporal Dependencies; Advanced Forecasting and Prediction Models; Cluster-based Forecasting; Explanation Methods for Time Series Classification; Multimodal Meta-Learning for Time Series Regression; and Multivariate Time Series Anomaly Detection. This book constitutes the refereed proceedings of the 6th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2021, held during September 13-17, 2021. Classification of Univariate and Multivariate Time Series; and Multivariate Time Series Anomaly Detection. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9783030914448

Bibliographic Details

• Title: Advanced Analytics and Learning on Temporal Data (Paperback)
• Publisher: Springer Nature Switzerland AG, Cham
• Publication Date: 2021
• Language: English
• ISBN 10: 3030914445
• ISBN 13: 9783030914448
• Binding: Paperback
• Edition: 1st Edition
• Condition: new

About this title

Synopsis

This book constitutes the refereed proceedings of the 6th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2021, held during September 13-17, 2021. The workshop was planned to take place in Bilbao, Spain, but was held virtually due to the COVID-19 pandemic.

The 12 full papers presented in this book were carefully reviewed and selected from 21 submissions. They focus on the following topics: Temporal Data Clustering; Classification of Univariate and Multivariate Time Series; Multivariate Time Series Co-clustering; Efficient Event Detection; Modeling Temporal Dependencies; Advanced Forecasting and Prediction Models; Cluster-based Forecasting; Explanation Methods for Time Series Classification; Multimodal Meta-Learning for Time Series Regression; and Multivariate Time Series Anomaly Detection.