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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

  • Conference proceedings
  • © 2021

Overview

Editors:
  1. Vincent Lemaire

    1. Orange Labs, Lannion, France

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  2. Simon Malinowski

    1. University of Rennes, Rennes, France

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  3. Anthony Bagnall

    1. University of East Anglia, Norwich, UK

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  4. Thomas Guyet

    1. Inria Grenoble - Rhône-Alpes, Villeurbanne, France

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  5. Romain Tavenard

    1. University of Rennes, Rennes, France

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  6. Georgiana Ifrim

    1. University College Dublin, Dublin, Ireland

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13114)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: AALTD 2021.

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Table of contents (12 papers)

  1. Front Matter

    Pages i-x
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  2. Oral Presentation

    1. Front Matter

      Pages 1-1
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    2. Ranking by Aggregating Referees: Evaluating the Informativeness of Explanation Methods for Time Series Classification

      • Surabhi Agarwal, Trang Thu Nguyen, Thach Le Nguyen, Georgiana Ifrim
      Pages 3-20

    3. State Space Approximation of Gaussian Processes for Time Series Forecasting

      • Alessio Benavoli, Giorgio Corani
      Pages 21-35

    4. Fast Channel Selection for Scalable Multivariate Time Series Classification

      • Bhaskar Dhariyal, Thach Le Nguyen, Georgiana Ifrim
      Pages 36-54

    5. Temporal Phenotyping for Characterisation of Hospital Care Pathways of COVID19 Patients

      • Mathieu Chambard, Thomas Guyet, Yên-Lan NGuyen, Etienne Audureau
      Pages 55-70

    6. Non-parametric Multivariate Time Series Co-clustering Model Applied to Driving-Assistance Systems Validation

      • Etienne Goffinet, Mustapha Lebbah, Hanane Azzag, Giraldi Loïc, Anthony Coutant
      Pages 71-87

    7. TRAMESINO: Traffic Memory System for Intelligent Optimization of Road Traffic Control

      • Cristian Axenie, Rongye Shi, Daniele Foroni, Alexander Wieder, Mohamad Al Hajj Hassan, Paolo Sottovia et al.
      Pages 88-103

    8. Detection of Critical Events in Renewable Energy Production Time Series

      • Laurens P. Stoop, Erik Duijm, Ad Feelders, Machteld van den Broek
      Pages 104-119

  3. Poster Presentation

    1. Front Matter

      Pages 121-121
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    2. Multimodal Meta-Learning for Time Series Regression

      • Sebastian Pineda Arango, Felix Heinrich, Kiran Madhusudhanan, Lars Schmidt-Thieme
      Pages 123-138

    3. Cluster-Based Forecasting for Intermittent and Non-intermittent Time Series

      • Tom van de Looij, Mozhdeh Ariannezhad
      Pages 139-154

    4. State Discovery and Prediction from Multivariate Sensor Data

      • Olli-Pekka Rinta-Koski, Miki Sirola, Le Ngu Nguyen, Jaakko Hollmén
      Pages 155-169

    5. RevDet: Robust and Memory Efficient Event Detection and Tracking in Large News Feeds

      • Abdul Hameed Azeemi, Muhammad Hamza Sohail, Talha Zubair, Muaz Maqbool, Irfan Younas, Omair Shafiq
      Pages 170-185

    6. From Univariate to Multivariate Time Series Anomaly Detection with Non-Local Information

      • Julien Audibert, Sébastien Marti, Frédéric Guyard, Maria A. Zuluaga
      Pages 186-194

  4. Back Matter

    Pages 195-195
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Other volumes

  1. Advanced Analytics and Learning on Temporal Data

Keywords

About this book

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.

 

 


Editors and Affiliations

  • Orange Labs, Lannion, France

    Vincent Lemaire

  • University of Rennes, Rennes, France

    Simon Malinowski, Romain Tavenard

  • University of East Anglia, Norwich, UK

    Anthony Bagnall

  • Inria Grenoble - Rhône-Alpes, Villeurbanne, France

    Thomas Guyet

  • University College Dublin, Dublin, Ireland

    Georgiana Ifrim

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