Advanced Analytics and Learning on Temporal Data

This book constitutes the refereed proceedings of the 4th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2019, held in Würzburg, Germany, in September 2019.
The 7 full papers presented together with 9 poster papers were carefully reviewed and selected from 31 submissions. The papers cover topics such as temporal data clustering; classification of univariate and multivariate time series; early classification of temporal data; deep learning and learning representations for temporal data; modeling temporal dependencies; advanced forecasting and prediction models; space-temporal statistical analysis; functional data analysis methods; temporal data streams; interpretable time-series analysis methods; dimensionality reduction, sparsity, algorithmic complexity and big data challenge; and bio-informatics, medical, energy consumption, on temporal data.

• time series analysis
• spatial-temporal systems
• supervised learning by classification
• classification and regression trees
• batch learning
• instance-based learning
• boosting
• forecasting
• computer networks
• data communication systems
• databases
• image processing
• internet
• machine learning
• network protocols
• sensors
• signal processing
• classification
• data mining
• computer hardware

• Orange Labs, Lannion, France

  Vincent Lemaire

• Inria, University of Rennes, Rennes, France

  Simon Malinowski

• University of East Anglia, Norwich, UK

  Anthony Bagnall

• Orange Labs, Châtillon, France

  Alexis Bondu

• Irisa, Agrocampus Ouest, Rennes, France

  Thomas Guyet

• University of Rennes 2, Rennes, France

  Romain Tavenard

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