Advanced Analytics and Learning on Temporal Data: 5th ECML PKDD Workshop, AALTD 2020, Ghent, Belgium, September 18, 2020, Revised Selected Papers 3030657418, 9783030657413

Table of contents :
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
4.2 Comparison of Multiple Explanation Methods
4.3 Sanity Checks for Experiment Results
5 Discussion
6 Conclusion
References
Poster Presentation
Temporal Exceptional Model Mining Using Dynamic Bayesian Networks
1 Introduction
2 Motivating Example: The Business Process Intelligence Challenge
3 Temporal Exceptional Model Mining
3.1 Temporal Targets
3.2 Subgroups
3.3 Problem Statement
4 Exceptional Dynamic Bayesian Networks
4.1 Dynamic Bayesian Networks
4.2 Distance Function
4.3 Scoring Function
4.4 Exceptional Subgroups
4.5 Distribution of False Discoveries
4.6 Subgroup Search
4.7 Exceptionality Test
5 Experiments with Simulated Data
5.1 Data Generating Procedure
5.2 Evaluation
5.3 Results
5.4 Impact of (dis)similar Models on Prediction
6 Data of Funding Applications
6.1 Data
6.2 Discovered Subgroups
6.3 Comparison to Previous Analyses
6.4 Subgroup Differences
7 Related Work
8 Conclusions
References
``J'veux du Soleil'' Towards a Decade of Solar Irradiation Data (La Réunion Island, SW Indian Ocean)
1 Introduction
2 Data Acquisition
2.1 Measuring Irradiation and Meteorological Parameters
3 Available Data
4 Valuable Applications
5 Conclusion
A Technical Setup
References
Visual Analytics for Extracting Trends from Spatio-temporal Data
1 Introduction
2 Rationale and Related Work
2.1 Rationale
2.2 Related Work
3 Methods
3.1 Cluster Analysis
3.2 Temporal Fingerprinting Through Circular Heat Maps
3.3 Spatio-temporal Comparison Through Circular Heat Map Subtraction
3.4 Temporal Behaviour Characterisation Through Label Maps
4 Case Study on Brussels Traffic
4.1 Data
4.2 Unravelling Volume Patterns of Brussels Traffic
4.3 Insightful Blueprints of Brussels Traffic
5 Conclusion and Future Work
References
Layered Integration Approach for Multi-view Analysis of Temporal Data
1 Introduction
2 Related Work and Rationale
2.1 Challenges Related to Real-World Datasets
2.2 Multi-view Learning
3 Use Case Context and Ambition
4 Methods and Proposed Approach
4.1 Clustering Analysis
4.2 Kernel Density Estimation (KDE)
4.3 Hypercube Binning Approach
4.4 Layered Multi-view Analysis: General Approach
4.5 Layered Multi-view Analysis: Instantiated in the Use Case
5 Dataset and Implementation
5.1 Data Preprocessing
5.2 Implementation and Availability
6 Results and Discussion
6.1 Individual Analysis Layer: Operating Mode Characterisation
6.2 Mediation Layer: Performance Profiling
6.3 Integration Layer: Fleet-Wide Performance Labelling
7 Conclusion and Future Work
References
Real-Time Outlier Detection in Time Series Data of Water Sensors
1 Introduction
2 Data Overview
3 Experiment Setup
3.1 Outlier Detection Pipeline
3.2 Synthetic Evaluation
4 Modelling and Hyper-Parameter Tuning
4.1 Regression-Based Models
4.2 Neural Network-Based Approaches
4.3 Direct Classification Model: Isolation Forests (IF)
5 Results
5.1 Illustrative Examples: Univariate Results
5.2 Illustrative Example: Multivariate Results QR-MLP
5.3 Comparison of Univariate and Multivariate Modelling Techniques
5.4 Comparison of Multivariate Modelling Techniques
5.5 Practical Impact
6 Conclusion and Future Work
References
Lightweight Temporal Self-attention for Classifying Satellite Images Time Series
1 Introduction
2 Method
2.1 Multi-headed Self-attention
2.2 Lightweight Attention
2.3 Spatio-Temporal Classifier
3 Numerical Experiment
3.1 Dataset
3.2 Metric and Protocol
3.3 Evaluated Methods
3.4 Analysis
3.5 Ablation Study and Robustness Assessment
3.6 Computational Complexity
4 Conclusion
References
Creating and Characterising Electricity Load Profiles of Residential Buildings
1 Introduction
2 Related Work
3 Smart Meter Characterisation and Classification Problem
4 Methodology
4.1 Time Series Clustering
4.2 Survey Classification
5 Experimental Results
5.1 Feature Vectors
5.2 Behaviour Clusters
5.3 Cluster Classification
5.4 Feature Importance
6 Conclusions
A Survey Questions
References
Trust Assessment on Streaming Data: A Real Time Predictive Approach
1 Introduction
2 Related Work
3 Data Trustworthiness Online Model: DTOM
3.1 Problem Statement
3.2 Design and Implementation
3.3 Online Ensemble Regression
4 Experimentation
4.1 Experimental Dataset
4.2 Evaluation
4.3 Results
5 Conclusion
References
A Feature Selection Method for Multi-dimension Time-Series Data
1 Introduction
2 Feature Selection
2.1 Correlation Based Feature Selection Using Mutual Information
2.2 Feature Selection for Time-Series Data
3 CFS for Time-Series Data
4 Evaluation
4.1 Data Sets
4.2 Merit Score Evaluation
4.3 Feature Subset Selection
5 Conclusions and Future Work
References
Author Index