Detecting Outlying Time-Series with Global Alignment Kernels
| Vortragende(r) | Haiko Thiessen | |
|---|---|---|
| Vortragstyp | Proposal | |
| Betreuer(in) | Florian Kalinke | |
| Termin | Fr 11. Dezember 2020 | |
| Vortragssprache | ||
| Vortragsmodus | ||
| Kurzfassung | Using outlier detection algorithms, e.g., Support Vector Data Description (SVDD), for detecting outlying time-series usually requires extracting domain-specific attributes. However, this indirect way needs expert knowledge, making SVDD impractical for many real-world use cases. Incorporating "Global Alignment Kernels" directly into SVDD to compute the distance between time-series data bypasses the attribute-extraction step and makes the application of SVDD independent of the underlying domain.
In this work, we propose a new time-series outlier detection algorithm, combining "Global Alignment Kernels" and SVDD. Its outlier detection capabilities will be evaluated on synthetic data as well as on real-world data sets. Additionally, our approach's performance will be compared to state-of-the-art methods for outlier detection, especially with regard to the types of detected outliers. | |