- Titel
- Challenges for Context-Driven Time Series Forecasting
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-811554
- Quellenangabe
- Journal of Data and Information Quality
Erscheinungsjahr: 2016
Jahrgang: 7
Heft: 1-2
Seiten: 1-4
E-ISSN: 1936-1963
Artikelnummer: 5 - Erstveröffentlichung
- 2016
- Abstract (EN)
- Predicting time series is a crucial task for organizations, since decisions are often based on uncertain information. Many forecasting models are designed from a generic statistical point of view. However, each real-world application requires domain-specific adaptations to obtain high-quality results. All such specifics are summarized by the term of context. In contrast to current approaches, we want to integrate context as the primary driver in the forecasting process. We introduce context-driven time series forecasting focusing on two exemplary domains: renewable energy and sparse sales data. In view of this, we discuss the challenge of context integration in the individual process steps.
- Andere Ausgabe
- Link zum Artikel der zuerst in der Zeitschrift 'Journal of Data and Information Quality' bei ACM erschienen ist.
DOI: 10.1145/2896822 - Freie Schlagwörter (DE)
- Zeitreihen, Vorhersage, Kontext
- Freie Schlagwörter (EN)
- Time Series, Forecasting, Context
- Klassifikation (DDC)
- 020
- 004
- Verlag
- ACM, New York
- Version / Begutachtungsstatus
- angenommene Version / Postprint / Autorenversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-811554
- Veröffentlichungsdatum Qucosa
- 10.01.2023
- Dokumenttyp
- Artikel
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
