Advanced Analytics and Learning on Temporal Data - 7th ECML PKDD Workshop, AALTD 2022, Grenoble, France, September 19-23, 2022, Revised Selected Papers

This book constitutes the refereed proceedings of the 7th ECML PKDD Workshop, AALTD 2022, held in Grenoble, France, during September 19-23, 2022.
The 12 full papers included in this book were carefully reviewed and selected from 21 submissions. They were organized in topical sections as follows: Oral presentation and poster presentation.

List of contents

Oral Presentation.- Adjustable Context-aware Transformer.- Clustering of time series based on forecasting performance of global models.- Experimental study of time series forecasting methods for groundwater level prediction.- Fast Time Series Classification with Random Symbolic Subsequences.- RESIST: Robust Transformer for Unsupervised Time Series Anomaly Detection.- Window Size Selection In Unsupervised Time Series Analytics: A Review and Benchmark.- Poster Presentation.- Application of Attention mechanism combined with Long Short-Term Memory for forecasting Dissolved Oxygen in Ganga River.- Data Augmentation for Time Series Classification with Deep Learning.- Dimension selection strategies for multivariate time series classification with HIVE-COTEv2.0.- EDGAR: Embedded Detection of Gunshots by AI in Real-time.- Identification of the Best Accelerometer Features and Time-scale to Detect Disturbances in Calves.- ODIN AD: a frameworksupporting the life-cycle of time series anomaly detection applications.

Product details

• Assisted by: Anthony Bagnall (Editor), Thomas Guyet (Editor), Georgiana Ifrim (Editor), Vincent Lemaire (Editor), Simon Malinowski (Editor), Simon Malinowski et al (Editor), Patrick Shafer (Editor)
• Publisher: Springer, Berlin
• Languages: English
• Product format: Paperback / Softback
• Released: 21.03.2023
• EAN: 9783031243776
• ISBN: 978-3-0-3124377-6
• No. of pages: 197
• Dimensions: 155 mm x 11 mm x 235 mm
• Illustrations: XVI, 197 p. 86 illus., 58 illus. in color.
• Series: Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence
• Subject: Natural sciences, medicine, IT, technology > IT, data processing > IT