Detecting Outlying Time-Series with Global Alignment Kernels: Unterschied zwischen den Versionen
Keine Bearbeitungszusammenfassung |
Keine Bearbeitungszusammenfassung |
||
Zeile 5: | Zeile 5: | ||
|betreuer=Florian Kalinke | |betreuer=Florian Kalinke | ||
|termin=Institutsseminar/2020-12-11 | |termin=Institutsseminar/2020-12-11 | ||
|kurzfassung=Using outlier detection algorithms | |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. In this work, a new algorithm based on "Global Alignment Kernels" and SVDD is developed. 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 if our approach detects different outliers. | ||
}} | }} |
Version vom 2. Dezember 2020, 16:16 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. In this work, a new algorithm based on "Global Alignment Kernels" and SVDD is developed. 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 if our approach detects different outliers. |