- Titel
- Generating What-If Scenarios for Time Series Data
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-804761
- Konferenz
- SSDBM '17: 29th International Conference on Scientific and Statistical Database Management. Chicago, 27.-29. Juni 2017
- Quellenangabe
- SSDBM '17: Proceedings of the 29th International Conference on Scientific and Statistical Database Management
Herausgeber: Association for Computing Machinery
Herausgeber: Alok Choudhary
Erscheinungsort: New York
Verlag: ACM
Erscheinungsjahr: 2017
Seiten: 1-12
ISBN: 978-1-4503-5282-6
Artikelnummer: 3 - Erstveröffentlichung
- 2017
- Abstract (EN)
- Time series data has become a ubiquitous and important data source in many application domains. Most companies and organizations strongly rely on this data for critical tasks like decision-making, planning, predictions, and analytics in general. While all these tasks generally focus on actual data representing organization and business processes, it is also desirable to apply them to alternative scenarios in order to prepare for developments that diverge from expectations or assess the robustness of current strategies. When it comes to the construction of such what-if scenarios, existing tools either focus on scalar data or they address highly specific scenarios. In this work, we propose a generally applicable and easy-to-use method for the generation of what-if scenarios on time series data. Our approach extracts descriptive features of a data set and allows the construction of an alternate version by means of filtering and modification of these features.
- Andere Ausgabe
- Link zum Artikel, der zuerst in der ACM Digital Library erschienen ist.
DOI: 10.1145/3085504.3085507 - Freie Schlagwörter (DE)
- Was-wäre-wenn-Szenario, Was-wäre-wenn-Analyse, hypothetische Abfrage, Zeitreihenanalyse, Geschäftsanalytik
- Freie Schlagwörter (EN)
- what-if scenario, what-if analysis, hypothetical query, time series analysis, business analytics
- 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-qucosa2-804761
- Veröffentlichungsdatum Qucosa
- 18.08.2022
- Dokumenttyp
- Konferenzbeitrag
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
