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Advanced Analytics and Learning on Temporal Data : 6th ECML PKDD Workshop, AALTD 2021, Bilbao, Spain, September 13, 2021, Revised Selected Papers / edited by Vincent Lemaire, Simon Malinowski, Anthony Bagnall, Thomas Guyet, Romain Tavenard, Georgiana Ifrim.
- Format:
- Book
- Series:
- Computer Science (SpringerNature-11645)
- Lecture notes in computer science. Lecture notes in artificial intelligence ; 13114
- Lecture Notes in Artificial Intelligence ; 13114
- Language:
- English
- Subjects (All):
- Artificial intelligence.
- Data mining.
- Computer networks.
- Social sciences-Data processing.
- Education-Data processing.
- Artificial Intelligence.
- Data Mining and Knowledge Discovery.
- Computer Communication Networks.
- Computer Application in Social and Behavioral Sciences.
- Computers and Education.
- Local Subjects:
- Artificial Intelligence.
- Data Mining and Knowledge Discovery.
- Computer Communication Networks.
- Computer Application in Social and Behavioral Sciences.
- Computers and Education.
- Physical Description:
- 1 online resource (X, 195 pages) : 68 illustrations, 57 illustrations in color.
- Edition:
- 1st ed. 2021.
- Contained In:
- Springer Nature eBook
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Springer, 2021.
- System Details:
- text file PDF
- Summary:
- 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. .
- Contents:
- Oral Presentation
- Ranking by Aggregating Referees: Evaluating the Informativeness of Explanation Methods for Time Series Classification
- State Space approximation of Gaussian Processes for time-series forecasting
- Fast Channel Selection for Scalable Multivariate Time Series Classification
- Temporal phenotyping for characterisation of hospital care pathways of COVID patients
- A New Multivariate Time Series Co-clustering Non-Parametric Model Applied to Driving-Assistance Systems Validation
- TRAMESINO: Trainable Memory System for Intelligent Optimization of Road Traffic Control
- Detection of critical events in renewable energy production time series
- Poster Presentation
- Multimodal Meta-Learning for Time Series Regression
- Cluster-based Forecasting for Intermittent and Non-intermittent Time Series
- State discovery and prediction from multivariate sensor data
- RevDet: Robust and Memory Efficient Event Detection and Tracking in Large News Feeds
- From Univariate to Multivariate Time Series Anomaly Detection with Non-Local Information.
- Other Format:
- Printed edition:
- ISBN:
- 978-3-030-91445-5
- 9783030914455
- Access Restriction:
- Restricted for use by site license.
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