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Table of contents
List of Figures
List of Tables
1 Introduction
1.1 Context and Motivations
1.2 Contributions
1.3 Structure of thesis
1.4 Publications
1.5 Challenge participation
2 Anomaly detection in time-series
2.1 Time Series
2.1.1 Univariate vs Multivariate
2.1.2 Decomposition of a time series
2.1.2.1 Trend
2.1.2.2 Seasonality
2.1.2.3 Level
2.1.2.4 Noise
2.1.3 Stationarity
2.2 Anomaly Detection
2.2.1 Types of Anomalies in Time Series
2.2.2 Supervised vs Unsupervised method
2.2.3 Taxonomy
2.2.4 Conventional methods
2.2.4.1 Control Charts methods
2.2.4.2 Forecast methods
2.2.4.3 Decomposition methods
2.2.4.4 Similarity-search approach
2.2.5 Machine learning-based methods
2.2.5.1 Isolation methods
2.2.5.2 Neighbourhood-based methods
2.2.5.3 Domain-based methods
2.2.6 Deep learning-based methods
3 Unsupervised Anomaly Detection on Multivariate Time Series
3.1 Introduction
3.2 Auto-Encoders and Generative Adversarial Networks limitations
3.3 UnSupervised Anomaly Detection (USAD)
3.3.1 Method
3.3.2 Implementation
3.3.3 Experimental setup
3.3.3.1 Datasets
3.3.3.2 Feasibility study: Orange’s dataset
3.3.3.3 Evaluation Metrics
3.3.4 Experiments and Results
3.3.4.1 Overall performance
3.3.4.2 Effect of parameters
3.3.4.3 Training time
3.3.4.4 Ablation Study
3.3.4.5 Feasibility study
3.4 Conclusion
4 From Univariate to Multivariate Time Series Anomaly Detection with Non-Local Information
4.1 Introduction
4.2 Related works
4.3 From univariate to multivariate time series
4.4 Experiments and Results
4.4.1 Datasets
4.4.2 Experimental setup
4.4.2.1 Implementation.
4.4.3 Results
4.5 Discussion and Conclusions
5 Are Deep Neural Networks Methods Needed for Anomaly Detection on Multivariate Time Series?
5.1 Introduction
5.2 Related work
5.3 Experimental setup
5.3.1 Public Datasets
5.3.2 Evaluation Metrics
5.4 Experiments and Results
5.4.1 Benchmark Performance
5.4.2 Analysis of WADI
5.4.3 Impact of training set size
5.4.4 Discussion
5.5 Conclusion
6 Conclusion and Perspectives
6.1 Conclusion
6.2 Perspectives
References
