Unsupervised temporal anomaly detection
| dc.contributor.advisor | Müller, Emmanuel | |
| dc.contributor.author | Li, Bin | |
| dc.contributor.referee | Gama, João | |
| dc.date.accepted | 2024-07-07 | |
| dc.date.accessioned | 2024-08-02T09:39:52Z | |
| dc.date.available | 2024-08-02T09:39:52Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Anomaly detection becomes essential across diverse domains. Data is usually collected sequentially in real-world applications such as sensor records and network logs. Consequently, a major challenge in anomaly detection is the real-time volatile sequential abnormal events. Recent research on time series has gained supreme advancements, leveraging the vast development of deep models like recurrent neural networks and transformers. However, most existing deep models focus on static time series while neglecting the dynamic streaming feature inherent in real-world deployment. A critical issue arises from the potential occurrence of distributional drift in streaming data, after which the pre-trained models become invalid. Furthermore, as machine learning models are applied in the safety-crucial fields like autonomous vehicles and medical diagnoses, the trustworthiness of model predictions becomes a growing concern. A desired anomaly detector is expected to both predict and interpret the abnormal events. This dissertation focuses on the intersecting research area between time series and data stream anomaly detection as well as their interpretability. We first develop a contrastivelearning- based self-supervised approach for time series anomaly detection, contributing to the effective representation learning of time series anomalies without labels. Subsequently, we investigate a novel concept drift detection approach for identifying correlation changes in the data stream. We also propose a state-transition-aware online anomaly detection framework for data streams. Finally, we delve into the necessary properties of time series interpreters, including cohesiveness, consistency, and robustness. We also showcase an example-based interpreter for reconstruction-based anomaly detection models, which provides intuitive and contrastive explanations of the reasons behind anomalies. The proposed approaches are rigorously evaluated on various popular real-world benchmark datasets and simulations. | en |
| dc.identifier.uri | http://hdl.handle.net/2003/42629 | |
| dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-24465 | |
| dc.language.iso | en | de |
| dc.subject | Time series | en |
| dc.subject | Data stream | en |
| dc.subject | Anomaly detection | en |
| dc.subject | Interpretability | en |
| dc.subject.ddc | 004 | |
| dc.subject.rswk | Zeitreihenanalyse | de |
| dc.subject.rswk | Datenstrom | de |
| dc.subject.rswk | Anomalieerkennung | de |
| dc.subject.rswk | Interpretierer | de |
| dc.title | Unsupervised temporal anomaly detection | en |
| dc.title.alternative | Time series, data stream, and interpretability | en |
| dc.type | Text | de |
| dc.type.publicationtype | PhDThesis | de |
| dcterms.accessRights | open access | |
| eldorado.secondarypublication | false | de |