Detecting Outlying Time-Series with Global Alignment Kernels: Unterschied zwischen den Versionen

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|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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Version vom 2. Dezember 2020, 09:31 Uhr

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.