Temporal pattern mining – Rule discovery

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Table of contents

1 Introduction 
I Learning from Time Series 
2 Machine Learning on Time Series 
2.1 Definitions & Notations
2.2 Overview of the time series mining field
2.2.1 Motif discovery
2.2.2 Time series retrieval
2.2.3 Clustering
2.2.4 Temporal pattern mining – Rule discovery
2.2.5 Anomaly detection
2.2.6 Summarization
2.2.7 Classification
2.3 Relationships between fields
2.4 Time series mining raises specific issues
2.4.1 A time series is not a suitable feature vector for machine learning
2.5 Train machine learning algorithms on time series
2.5.1 Time-based classification
2.5.2 Feature-based classification
2.6 Conclusions
3 Time Series Representations 
3.1 Concept of time series representation
3.2 Time-based representations
3.2.1 Piecewise Representations
3.2.2 Symbolic representations
3.2.3 Transform-based representations
3.3 Feature-based representations
3.3.1 Overall principle
3.3.2 Brief overview of features from time series analysis
3.4 Motif-based representations
3.4.1 Recurrent motif
3.4.2 Surprising or anomalous motif
3.4.3 Discriminant motif
3.4.4 Set of motifs and Sequence-based representation
3.5 Ensemble of representations
3.6 Conclusions
II Our Contribution: a Discriminant Motif-Based Representation 
4 Motif Discovery for Classification 
4.1 Time series shapelet principle
4.2 Computational complexity of the shapelet discovery
4.2.1 Early abandon & Pruning non-promising candidates
4.2.2 Distance caching
4.2.3 Discovery from a rough representation of the time series
4.2.4 Alternative quality measures
4.2.5 Learning shapelet using gradient descent
4.2.6 Infrequent subsequences as shapelet candidates
4.2.7 Avoid the evaluation of similar candidates
4.3 Various shapelet-based algorithms
4.3.1 The original approach: the shapelet-tree
4.3.2 Variants of the shapelet-tree
4.3.3 Shapelet transform
4.3.4 Other distance measures
4.3.5 Shapelet on multivariate time series
4.3.6 Early time series classification
4.4 Conclusions
5 Discriminant Motif-Based Representation 
5.1 Notations
5.2 Subsequence transformation principle
5.3 Motif-based representation
5.4 Conclusions
6 Scalable Discovery of Discriminant Motifs 
6.1 An intractable exhaustive discovery among S
6.2 Subsequence redundancy in S
6.3 A random sub-sampling of S is a solution
6.4 Discussion on j ^ Sj the number of subsequences to draw
6.5 Experimentation: impact of random subsampling
6.6 Conclusions
7 EAST-Representation 
7.1 Discovery as a feature selection problem
7.2 Experimentation
7.2.1 Objective
7.2.2 Setup
7.2.3 Datasets
7.2.4 Results
7.3 Discussion
7.4 Conclusions
III Industrial Applications 
8 Presentation of the industrial use cases 
8.1 Context of the industrial use cases
8.1.1 Steel production & Process monitoring
8.1.2 Types of data
8.1.3 Industrial problematic formalization
8.2 Description of the use cases
8.2.1 1st use case: sliver defect, detection of inclusions at continuous casting
8.2.2 2nd use case: detection of mechanical properties scattering
8.3 Conclusions
9 Benchmark on the industrial use cases 
9.1 Experimental procedure
9.1.1 Feature vector engineering for the time series
9.1.2 Learning stack
9.1.3 Classification performance evaluation
9.2 Results
9.2.1 Classification performances
9.2.2 Illustration of discovered EAST-shapelets
9.3 Computational performances
9.4 Conclusions
IV Conclusions 
10 Conclusions & Perspectives 
Bibliography