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Advanced analytics and learning on temporal data : 5th ECML PKDD Workshop, AALTD 2020, Ghent, Belgium, September 18, 2020, Revised selected papers / Vincent Lemaire, Simon Malinowski, Anthony Bagnall, Thomas Guyet, Romain Tavenard, Georgiana Ifrim (eds.).

By: Contributor(s): Material type: TextTextSeries: Lecture notes in computer science ; 12588. | LNCS sublibrary. SL 7, Artificial intelligence.Publication details: Cham : Springer, 2021.Description: 1 online resource (240 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783030657420
  • 3030657426
Other title:
  • AALTD 2020
Subject(s): Genre/Form: Additional physical formats: Print version:: Advanced Analytics and Learning on Temporal Data : 5th ECML PKDD Workshop, AALTD 2020, Ghent, Belgium, September 18, 2020, Revised Selected Papers.DDC classification:
  • 006.3/1 23
LOC classification:
  • Q325.5
Online resources:
Contents:
Intro -- Preface -- Workshop Description -- Organization -- Contents -- Oral Presentation -- On the Usage and Performance of the Hierarchical Vote Collective of Transformation-Based Ensembles Version 1.0 (HIVE-COTE v1.0) -- 1 Introduction -- 2 HIVE-COTE 1.0 Design -- 2.1 Ensemble Structure -- 2.2 Time Series Forest (TSF) -- 2.3 Random Interval Spectral Ensemble (RISE) -- 2.4 Bag of SFA Symbols (BOSS) -- 2.5 Shapelet Transform Classifier (STC) -- 3 HIVE-COTE 1.0 Usability -- 3.1 Java Implementation of HIVE-COTE 1.0 in tsml -- 3.2 Python Implementation of HIVE-COTE 1.0 in sktime
4 Performance -- 5 Conclusions -- References -- Ordinal Versus Nominal Time Series Classification -- 1 Introduction -- 2 Background -- 2.1 Time Series Shapelets -- 2.2 Ordinal Classification -- 3 Experimental Results and Discussion -- 3.1 TSOC Datasets -- 3.2 Experimental Settings -- 3.3 Results -- 3.4 Comparison Against the State-of-the-Art Algorithms in TSC -- 4 Conclusions -- References -- Generalized Chronicles for Temporal Sequence Classification -- 1 Introduction -- 2 Related Works -- 3 Discriminant Chronicle Mining -- 4 Generalized Discriminant Chronicles (GDC)
4.1 Taking Decisions with Generalized Discriminant Chronicles -- 4.2 Learning Generalized Discriminant Chronicles Classifiers -- 5 Examples of GDC Instances -- 6 Experiments -- 6.1 Experimental Setup -- 6.2 Results -- 7 Conclusion and Perspectives -- References -- Demand Forecasting in the Presence of Privileged Information -- 1 Introduction -- 2 Related Work -- 3 A Privileged Information-Aware Neural Network -- 3.1 Problem Statement -- 3.2 Architecture Overview -- 3.3 Architecture Details -- 3.4 Learning Process -- 4 Experimental Setup -- 5 Experimental Results
5.1 Capturing the Effects of Privileged Information -- 5.2 Comparison to Existing Approaches for Demand Forecasting -- 6 Conclusions and Future Work -- References -- GANNSTER: Graph-Augmented Neural Network Spatio-Temporal Reasoner for Traffic Forecasting -- 1 Introduction -- 2 Related Work -- 2.1 Traffic Forecasting -- 2.2 Graph Neural Networks -- 2.3 Graph Neural Networks for Traffic Forecasting -- 3 GANNSTER -- 3.1 Road Graph -- 3.2 Definitions -- 3.3 GANNSTER Model -- 4 Experimental Evaluation -- 4.1 MUSTARD-S -- 4.2 Experimental Settings -- 5 Results and Discussion
6 Conclusion and Future Work -- References -- A Model-Agnostic Approach to Quantifying the Informativeness of Explanation Methods for Time Series Classification -- 1 Introduction -- 2 Related Work -- 2.1 Time Series Classification -- 2.2 Explanation in Time Series Classification -- 2.3 Explanation in Other Machine Learning Domains -- 3 Research Methods -- 3.1 Explanation-Driven Perturbation -- 3.2 Method 1: Evaluating a Single Explanation Method -- 3.3 Method 2: Comparing Multiple Explanation Methods -- 3.4 Informativeness of an Explanation: An Evaluation Measure -- 4 Experiments
Summary: This book constitutes the refereed proceedings of the 4th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2019, held in Ghent, Belgium, in September 2020. The 15 full papers presented in this book were carefully reviewed and selected from 29 submissions. The selected papers are devoted to 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, Temporal Data.
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"This year, ECML-PKDD 2020, was planned to take place in Ghent, Belgium, during September 14-18, 2020, but due to the COVID-19 pandemic it was held in the same time period as a fully virtual event."--Preface

Intro -- Preface -- Workshop Description -- Organization -- Contents -- Oral Presentation -- On the Usage and Performance of the Hierarchical Vote Collective of Transformation-Based Ensembles Version 1.0 (HIVE-COTE v1.0) -- 1 Introduction -- 2 HIVE-COTE 1.0 Design -- 2.1 Ensemble Structure -- 2.2 Time Series Forest (TSF) -- 2.3 Random Interval Spectral Ensemble (RISE) -- 2.4 Bag of SFA Symbols (BOSS) -- 2.5 Shapelet Transform Classifier (STC) -- 3 HIVE-COTE 1.0 Usability -- 3.1 Java Implementation of HIVE-COTE 1.0 in tsml -- 3.2 Python Implementation of HIVE-COTE 1.0 in sktime

4 Performance -- 5 Conclusions -- References -- Ordinal Versus Nominal Time Series Classification -- 1 Introduction -- 2 Background -- 2.1 Time Series Shapelets -- 2.2 Ordinal Classification -- 3 Experimental Results and Discussion -- 3.1 TSOC Datasets -- 3.2 Experimental Settings -- 3.3 Results -- 3.4 Comparison Against the State-of-the-Art Algorithms in TSC -- 4 Conclusions -- References -- Generalized Chronicles for Temporal Sequence Classification -- 1 Introduction -- 2 Related Works -- 3 Discriminant Chronicle Mining -- 4 Generalized Discriminant Chronicles (GDC)

4.1 Taking Decisions with Generalized Discriminant Chronicles -- 4.2 Learning Generalized Discriminant Chronicles Classifiers -- 5 Examples of GDC Instances -- 6 Experiments -- 6.1 Experimental Setup -- 6.2 Results -- 7 Conclusion and Perspectives -- References -- Demand Forecasting in the Presence of Privileged Information -- 1 Introduction -- 2 Related Work -- 3 A Privileged Information-Aware Neural Network -- 3.1 Problem Statement -- 3.2 Architecture Overview -- 3.3 Architecture Details -- 3.4 Learning Process -- 4 Experimental Setup -- 5 Experimental Results

5.1 Capturing the Effects of Privileged Information -- 5.2 Comparison to Existing Approaches for Demand Forecasting -- 6 Conclusions and Future Work -- References -- GANNSTER: Graph-Augmented Neural Network Spatio-Temporal Reasoner for Traffic Forecasting -- 1 Introduction -- 2 Related Work -- 2.1 Traffic Forecasting -- 2.2 Graph Neural Networks -- 2.3 Graph Neural Networks for Traffic Forecasting -- 3 GANNSTER -- 3.1 Road Graph -- 3.2 Definitions -- 3.3 GANNSTER Model -- 4 Experimental Evaluation -- 4.1 MUSTARD-S -- 4.2 Experimental Settings -- 5 Results and Discussion

6 Conclusion and Future Work -- References -- A Model-Agnostic Approach to Quantifying the Informativeness of Explanation Methods for Time Series Classification -- 1 Introduction -- 2 Related Work -- 2.1 Time Series Classification -- 2.2 Explanation in Time Series Classification -- 2.3 Explanation in Other Machine Learning Domains -- 3 Research Methods -- 3.1 Explanation-Driven Perturbation -- 3.2 Method 1: Evaluating a Single Explanation Method -- 3.3 Method 2: Comparing Multiple Explanation Methods -- 3.4 Informativeness of an Explanation: An Evaluation Measure -- 4 Experiments

4.1 Experiment 1: Evaluation of a Single Explanation Method.

This book constitutes the refereed proceedings of the 4th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2019, held in Ghent, Belgium, in September 2020. The 15 full papers presented in this book were carefully reviewed and selected from 29 submissions. The selected papers are devoted to 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, Temporal Data.

Includes author index.

Online resource; title from PDF title page (SpringerLink, viewed February 17, 2021).

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