Thesis
Time-series anomaly detection via representation learning: A latent space approach
Washington State University
Master of Science (MS), Washington State University
05/2020
DOI:
https://doi.org/10.7273/000004236
Handle:
https://hdl.handle.net/2376/124627
Abstract
Anomaly detection over multivariate time-series is an important problem with numerous real-world applications in diverse domains including traffic monitoring, medical diagnosis, power outage detection etc. The goal is to automatically discover discrepancies in very few time steps of the, usually, enormous data. The key challenge in this task is that it requires a good representation to capture both temporal and spatial relationships in the time-series data. The simple approach of using hand-designed features to represent time-series data and applying traditional anomaly detection algorithms doesn't perform well. The family of reconstruction based deep generative models do not use the spatial and temporal information during the process of predicting anomalies. In this dissertation, we study a novel approach that combines the strengths of deep neural networks and traditional anomaly detection algorithms that operate over data represented as features. We propose Extraction of Views from Latent Space representation Architecture (EVLS-Arc), an architecture that allows us to learn latent space representation of the spatio-temporal information extracted from a sequenceto-sequence deep model that is pre-trained to fit nominal sequences. Subsequently, we employ this latent space representation to perform anomaly detection using traditional algorithms (e.g., [kappa]-Nearest Neighbor, Isolation Forest). The spatio-temporal information of time-series data is extracted in the form of views of the data from the latent space, namely, hidden state speed change and attention weight vectors. Our experimental results on diverse benchmark datasets show promising results and suggest further work is needed to improve the accuracy of this general approach.
Metrics
6 File views/ downloads
49 Record Views
Details
- Title
- Time-series anomaly detection via representation learning
- Creators
- Sriyandass Adidass
- Contributors
- Venkata Janardhan Rao Doppa (Advisor) - Washington State University, Electrical Engineering and Computer Science, School of
- Awarding Institution
- Washington State University
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University
- Identifiers
- 99900896423901842
- Language
- English
- Resource Type
- Thesis