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Advanced analytics and learning on temporal data : 7th ECML PKDD Workshop, AALTD 2022, Grenoble, France, September 19-23, 2022, Revised selected papers.

By: Contributor(s): Material type: TextTextSeries: LNCS sublibrary. SL 7, Artificial intelligence. | Lecture notes in computer science ; 13812.Publisher: Cham, Switzerland : Springer, 2023Description: 1 online resource (xvi, 197 pages) : illustrations (some colour)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783031243783
  • 3031243781
Subject(s): Genre/Form: Additional physical formats: Print version:: Advanced Analytics and Learning on Temporal DataDDC classification:
  • 006.3/1 23/eng/20230327
LOC classification:
  • Q325.5 .A38 2023
Online resources:
Contents:
Intro -- Preface -- Workshop Description -- Organization -- Causal Discovery in Observational Time Series (Invited Talk) -- Contents -- Oral Presentation -- Adjustable Context-Aware Transformer -- 1 Introduction -- 2 Problem Definition -- 3 Related Work -- 4 Methodology -- 4.1 Background: Issues Arising from Point-Wise Attention -- 4.2 Temporal Attention -- 4.3 Adjustable Context-Aware Attention -- 4.4 Efficient Adjustable Context-Aware Attention -- 4.5 Overarching Architecture -- 5 Experiments -- 5.1 Datasets -- 5.2 Evaluation Metrics -- 5.3 Baselines
5.4 Model Training and Hyperprarameters -- 5.5 Results and Discussion -- 6 Conclusion -- References -- Clustering of Time Series Based on Forecasting Performance of Global Models -- 1 Introduction -- 2 A Clustering Algorithm Based on Prediction Accuracy of Global Forecasting Models -- 3 Simulation Study -- 3.1 Experimental Design -- 3.2 Alternative Approaches and Assessment Criteria -- 3.3 Results and Discussion -- 4 Application to Real Data -- 5 Conclusions -- References -- Experimental Study of Time Series Forecasting Methods for Groundwater Level Prediction -- 1 Introduction
2 Data Collection -- 3 Groundwater Level Forecasting -- 3.1 Local Versus Global Time Series Forecasting -- 3.2 Considered Methods -- 4 Experimental Settings -- 4.1 Setup -- 4.2 Comparison Metrics -- 5 Results -- 5.1 Generalized Autoregressive Models Results -- 5.2 DeepAR-Based Models Results -- 5.3 Prophet-Based Models Results -- 5.4 Comparing the Three Groups of Models -- 5.5 Discussion -- 6 Conclusion -- References -- Fast Time Series Classification with Random Symbolic Subsequences -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Symbolic Representations of Time Series
3.2 MrSQM Variants -- 4 Evaluation -- 4.1 Experiment Setup -- 4.2 Sensitivity Analysis -- 4.3 MrSQM Versus State-of-the-Art Symbolic Time Series Classifiers -- 4.4 MrSQM Versus Other State-of-the-Art Time Series Classifiers -- 5 Conclusion -- References -- RESIST: Robust Transformer for Unsupervised Time Series Anomaly Detection -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 RESIST Architecture -- 3.2 Robust Training Loss -- 3.3 Hypotheses -- 4 Experiments and Results -- 4.1 Dataset Description -- 4.2 Data Preprocessing -- 4.3 Training and Testing Protocols
4.4 Training Parameter Settings and Evaluation Criteria -- 4.5 Results -- 5 Conclusion and Perspectives -- References -- Window Size Selection in Unsupervised Time Series Analytics: A Review and Benchmark -- 1 Introduction -- 2 Background and Related Work -- 2.1 Definitions -- 2.2 Anomaly Detection -- 2.3 Segmentation -- 2.4 Motif Discovery -- 3 Window Size Selection -- 3.1 Dominant Fourier Frequency -- 3.2 Highest Autocorrelation -- 3.3 Hybrids: AutoPeriod and RobustPeriod -- 3.4 Multi-Window-Finder -- 3.5 Summary Statistics Subsequence -- 4 Experimental Evaluation -- 4.1 Setup
Summary: This book constitutes the refereed proceedings of the 7th ECML PKDD Workshop, AALTD 2022, held in Grenoble, France, during September 1923, 2022. The 12 full papers included in this book were carefully reviewed and selected from 21 submissions. They were organized in topical sections as follows: Oral presentation and poster presentation.
Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
eBook eBook e-Library eBook LNCS Available
Total holds: 0

Includes bibliographical references (page xii-xiii) and author index.

Intro -- Preface -- Workshop Description -- Organization -- Causal Discovery in Observational Time Series (Invited Talk) -- Contents -- Oral Presentation -- Adjustable Context-Aware Transformer -- 1 Introduction -- 2 Problem Definition -- 3 Related Work -- 4 Methodology -- 4.1 Background: Issues Arising from Point-Wise Attention -- 4.2 Temporal Attention -- 4.3 Adjustable Context-Aware Attention -- 4.4 Efficient Adjustable Context-Aware Attention -- 4.5 Overarching Architecture -- 5 Experiments -- 5.1 Datasets -- 5.2 Evaluation Metrics -- 5.3 Baselines

5.4 Model Training and Hyperprarameters -- 5.5 Results and Discussion -- 6 Conclusion -- References -- Clustering of Time Series Based on Forecasting Performance of Global Models -- 1 Introduction -- 2 A Clustering Algorithm Based on Prediction Accuracy of Global Forecasting Models -- 3 Simulation Study -- 3.1 Experimental Design -- 3.2 Alternative Approaches and Assessment Criteria -- 3.3 Results and Discussion -- 4 Application to Real Data -- 5 Conclusions -- References -- Experimental Study of Time Series Forecasting Methods for Groundwater Level Prediction -- 1 Introduction

2 Data Collection -- 3 Groundwater Level Forecasting -- 3.1 Local Versus Global Time Series Forecasting -- 3.2 Considered Methods -- 4 Experimental Settings -- 4.1 Setup -- 4.2 Comparison Metrics -- 5 Results -- 5.1 Generalized Autoregressive Models Results -- 5.2 DeepAR-Based Models Results -- 5.3 Prophet-Based Models Results -- 5.4 Comparing the Three Groups of Models -- 5.5 Discussion -- 6 Conclusion -- References -- Fast Time Series Classification with Random Symbolic Subsequences -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Symbolic Representations of Time Series

3.2 MrSQM Variants -- 4 Evaluation -- 4.1 Experiment Setup -- 4.2 Sensitivity Analysis -- 4.3 MrSQM Versus State-of-the-Art Symbolic Time Series Classifiers -- 4.4 MrSQM Versus Other State-of-the-Art Time Series Classifiers -- 5 Conclusion -- References -- RESIST: Robust Transformer for Unsupervised Time Series Anomaly Detection -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 RESIST Architecture -- 3.2 Robust Training Loss -- 3.3 Hypotheses -- 4 Experiments and Results -- 4.1 Dataset Description -- 4.2 Data Preprocessing -- 4.3 Training and Testing Protocols

4.4 Training Parameter Settings and Evaluation Criteria -- 4.5 Results -- 5 Conclusion and Perspectives -- References -- Window Size Selection in Unsupervised Time Series Analytics: A Review and Benchmark -- 1 Introduction -- 2 Background and Related Work -- 2.1 Definitions -- 2.2 Anomaly Detection -- 2.3 Segmentation -- 2.4 Motif Discovery -- 3 Window Size Selection -- 3.1 Dominant Fourier Frequency -- 3.2 Highest Autocorrelation -- 3.3 Hybrids: AutoPeriod and RobustPeriod -- 3.4 Multi-Window-Finder -- 3.5 Summary Statistics Subsequence -- 4 Experimental Evaluation -- 4.1 Setup

4.2 Anomaly Detection

This book constitutes the refereed proceedings of the 7th ECML PKDD Workshop, AALTD 2022, held in Grenoble, France, during September 1923, 2022. The 12 full papers included in this book were carefully reviewed and selected from 21 submissions. They were organized in topical sections as follows: Oral presentation and poster presentation.

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