Advanced Analytics and Learning on Temporal Data : 7th ECML PKDD Workshop, AALTD 2022, Grenoble, France, September 19-23, 2022, Revised Selected Papers / / Thomas Guyet [and five others] (editors)

• Pubblicazione: Cham, Switzerland : , : Springer, , [2023]; ©2023
• Edizione: First edition.
• Descrizione fisica: 1 online resource (209 pages)
• Disciplina: 006.31
• Soggetto topico: Machine learning
• Persona (resp. second.): GuyetThomas
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 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 framework supporting the life-cycle of time series anomaly detection applications.
• Sommario/riassunto: 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.
• Titolo autorizzato: Advanced Analytics and Learning on Temporal Data
• ISBN: 3-031-24378-1
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910682565603321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture notes in computer science ; ; Volume 13812.