Detecting Outlying Time-Series with Global Alignment Kernels

Vortragende(r)	Haiko Thiessen
Vortragstyp	Proposal
Betreuer(in)	Florian Kalinke
Termin	Fr 11. Dezember 2020
Vortragsmodus
Kurzfassung	Using outlier detection algorithms e.g., SVDD, for detecting outlying Time-Series usually requires extracting domain-specific attributes. However, this indirect way requires expert knowledge, which makes SVDD in many use cases impractical. 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.

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