- Titel
- Feature-based Comparison and Generation of Time Series
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-804407
- Konferenz
- SSDBM '18: 30th International Conference on Scientific and Statistical Database Management. Bozen, 9. - 11. Juli 2018
- Quellenangabe
- SSDBM '18: Proceedings of the 30th International Conference on Scientific and Statistical Database Management
Herausgeber: Dimitris Sacharidis
Herausgeber: Johann Gamper
Herausgeber: Michael Böhlen
Erscheinungsort: New York
Verlag: ACM
Erscheinungsjahr: 2018
Seiten: 1-12
ISBN: 978-1-4503-6505-5
Artikelnummer: 20 - Erstveröffentlichung
- 2018
- Abstract (EN)
- For more than three decades, researchers have been developping generation methods for the weather, energy, and economic domain. These methods provide generated datasets for reasons like system evaluation and data availability. However, despite the variety of approaches, there is no comparative and cross-domain assessment of generation methods and their expressiveness. We present a similarity measure that analyzes generation methods regarding general time series features. By this means, users can compare generation methods and validate whether a generated dataset is considered similar to a given dataset. Moreover, we propose a feature-based generation method that evolves cross-domain time series datasets. This method outperforms other generation methods regarding the feature-based similarity.
- Andere Ausgabe
- Link zum Artikel, der zuerst in der ACM Digital Library erschienen ist.
DOI: 10.1145/3221269.3221293 - Freie Schlagwörter (DE)
- Zeitreihenbildung, Zeitreihenmerkmale, Ähnlichkeitsmaß
- Freie Schlagwörter (EN)
- time series generation, time series features, similarity measure
- Klassifikation (DDC)
- 004
- Verlag
- ACM, New York
- Förder- / Projektangaben
- European Commission (EC)
H2020 | IA
Generalized Operational FLEXibility for Integrating Renewables in the Distribution Grid
(GOFLEX)
ID: 731232 - Version / Begutachtungsstatus
- angenommene Version / Postprint / Autorenversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-804407
- Veröffentlichungsdatum Qucosa
- 17.08.2022
- Dokumenttyp
- Konferenzbeitrag
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
