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Advanced Analytics and Learning on Temporal Data: 7th ECML PKDD Workshop, AALTD 2022, Grenoble, France, September 19–23, 2022, Revised Selected Papers: Lecture Notes in Computer Science, cartea 13812

Editat de Thomas Guyet, Georgiana Ifrim, Simon Malinowski, Anthony Bagnall, Patrick Shafer, Vincent Lemaire
en Limba Engleză Paperback – 21 mar 2023
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.
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Specificații

ISBN-13: 9783031243776
ISBN-10: 3031243773
Pagini: 197
Ilustrații: XVI, 197 p. 86 illus., 58 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.31 kg
Ediția:1st ed. 2023
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence

Locul publicării:Cham, Switzerland

Cuprins

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.