Anomaly detection using time series forecasting with deep learning
| dc.contributor.author | Mathonsi, Thabang | |
| dc.date.accessioned | 2023-11-23T08:15:26Z | |
| dc.date.available | 2023-11-23T08:15:26Z | |
| dc.date.issued | 2022 | |
| dc.description | A thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy to the Faculty of Science, University of the Witwatersrand, 2022 | |
| dc.description.abstract | Anomaly detection is increasingly researched by the academic community because of its growing importance in applications such as, for instance, monitoring sensor readings in autonomous vehicles, or diagnosing potential medical risks in health data. This thesis presents solutions to the anomaly detection problem with the aid of multivariate time series forecasting, uncertainty quantification, and explainable and interpretable artificial intelligence. Challenges with existing deep learning-based time series anomaly detection approaches include (i) large sets of parameters that may be computationally intensive to tune for non-parsimonious models, (ii) returning too many false positives rendering the techniques impractical for use, (iii) requiring labelled datasets for training which are often not prevalent in real life, (iv) temporal dependence inherent in the data, as well as (v) complex dependency and cross-correlation between the covariates that may be hard to capture. An interpretable statistics and deep learning-based hybrid anomaly detection method is introduced, which overcomes these challenges. By systematically building anomaly detection machinery that is firstly good at forecasting, secondly satisfactorily quantifies the associated uncertainty, and thirdly, is interpretable, the presented methodology suggests that hybrid models show significant promise in terms of accuracy, efficiency, and practical usage. The extensive experimental results indicate that interpretable hybrid approaches can have a significant impact on anomaly detection as a research field, and the interpretability of such models can be useful for practitioners | |
| dc.description.librarian | PC(2023) | |
| dc.faculty | Faculty of Science | |
| dc.identifier.uri | https://hdl.handle.net/10539/37141 | |
| dc.language.iso | en | |
| dc.phd.title | PhD | |
| dc.school | Computer Science and Applied Mathematics | |
| dc.subject | Anomaly Detection | |
| dc.subject | Time Series Forecasting | |
| dc.subject | Deep Learning | |
| dc.title | Anomaly detection using time series forecasting with deep learning | |
| dc.type | Thesis |