Detecting Outlying Time-Series with Global Alignment Kernels

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Vortragende(r) Haiko Thiessen
Vortragstyp Proposal
Betreuer(in) Florian Kalinke
Termin Fr 11. Dezember 2020
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.