Advanced Analysis and Learning on Temporal Data

First ECML PKDD Workshop, AALTD 2015, Porto, Portugal, September 11, 2015, Revised Selected Papers

  • Ahlame Douzal-Chouakria
  • José A. Vilar
  • Pierre-François Marteau
Conference proceedings AALTD 2015

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9785)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 9785)

Table of contents

  1. Front Matter
    Pages I-X
  2. Time Series Representation and Compression

    1. Front Matter
      Pages 1-1
    2. Alexis Bondu, Marc Boullé, Antoine Cornuéjols
      Pages 3-16
    3. Adeline Bailly, Simon Malinowski, Romain Tavenard, Laetitia Chapel, Thomas Guyet
      Pages 17-30
  3. Time Series Classification and Clustering

    1. Front Matter
      Pages 47-47
    2. Borja Lafuente-Rego, Jose A. Vilar
      Pages 49-64
    3. Claudio Gallicchio, Alessio Micheli, Luca Pedrelli, Luigi Fortunati, Federico Vozzi, Oberdan Parodi
      Pages 65-77
    4. Fernando Mateo, Jordi Muñoz-Marí, Valero Laparra, Jochem Verrelst, Gustau Camps-Valls
      Pages 78-94
    5. Ricardo Andrade-Pacheco, Martin Mubangizi, John Quinn, Neil Lawrence
      Pages 95-110
  4. Metric Learning for Time Series Comparison

    1. Front Matter
      Pages 129-129
    2. Cao-Tri Do, Ahlame Douzal-Chouakria, Sylvain Marié, Michèle Rombaut
      Pages 131-143
    3. Saeid Soheily-Khah, Ahlame Douzal-Chouakria, Eric Gaussier
      Pages 144-156
    4. Marc Dupont, Pierre-François Marteau
      Pages 157-172
  5. Back Matter
    Pages 173-173

About these proceedings


This book constitutes the refereed proceedings of the First ECML PKDD Workshop, AALTD 2015, held in Porto, Portugal, in September 2016. 
The 11 full papers presented were carefully reviewed and selected from 22 submissions. The first part focuses on learning new representations and embeddings for time series classification, clustering or for dimensionality reduction. The second part presents approaches on classification and clustering with challenging applications on medicine or earth observation data. These works show different ways to consider temporal dependency in clustering or classification processes. The last part of the book is dedicated to metric learning and time series comparison, it addresses the problem of speeding-up the dynamic time warping or dealing with multi-modal and multi-scale metric learning for time series classification and clustering.



dimensionality reduction machine learning temporal data mining time series analysis time series representation classification fuzzy clustering graphical model kernel methods metric learning multivariate time series structure learning supervised classification SVM temporal data analysis temporal kernel temporal metrics time series classification time series clustering unsupervised classification

Editors and affiliations

  • Ahlame Douzal-Chouakria
    • 1
  • José A. Vilar
    • 2
  • Pierre-François Marteau
    • 3
  1. 1.Lab. d'Informatique de GrenobleUniversité GrenobleGrenobleFrance
  2. 2.Universidade da CorunaCorunaSpain
  3. 3.IRISAUniversité de Bretagne-SudVannesFrance

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science Computer Science (R0)
  • Print ISBN 978-3-319-44411-6
  • Online ISBN 978-3-319-44412-3
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site
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