- AutorIn
- Wolfgang Lehner Technische Universität Dresden, Fakultät Informatik, Institut für Systemarchitektur, Professur Datenbanken
- Claudio HartmannTechnische Universität Dresden, Fakultät Informatik, Institut für Systemarchitektur, Dresden Database Research Group
- Martin HahmannTechnische Universität Dresden, Fakultät Informatik, Institut für Systemarchitektur, Dresden Database Research Group
- Dirk Habich
- Titel
- CSAR: The Cross-Sectional Autoregression Model
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-821819
- Konferenz
- International Conference on Data Science and Advanced Analytics (DSAA). Tokyo, 19.-21.10.2017
- Quellenangabe
- 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA)
Erscheinungsort: New York, NY
Verlag: IEEE
Erscheinungsjahr: 2018
Seiten: 232-241 - Erstveröffentlichung
- 2018
- Abstract (EN)
- The forecasting of time series data is an integral component for management, planning, and decision making. Following the Big Data trend, large amounts of time series data are available in many application domains. The highly dynamic and often noisy character of these domains in combination with the logistic problems of collecting data from a large number of data sources, imposes new requirements on the forecasting process. A constantly increasing number of time series has to be forecasted, preferably with low latency AND high accuracy. This is almost impossible, when keeping the traditional focus on creating one forecast model for each individual time series. In addition, often used forecasting approaches like ARIMA need complete historical data to train forecast models and fail if time series are intermittent. A method that addresses all these new requirements is the cross-sectional forecasting approach. It utilizes available data from many time series of the same domain in one single model, thus, missing values can be compensated and accurate forecast results can be calculated quickly. However, this approach is limited by a rigid training data selection and existing forecasting methods show that adaptability of the model to the data increases the forecast accuracy. Therefore, in this paper we present CSAR a model that extends the cross-sectional paradigm by adding more flexibility and allowing fine grained adaptations to the analyzed data. In this way, we achieve an increased forecast accuracy and thus a wider applicability.
- Andere Ausgabe
- Link zum Artikel, der zuerst in der IEEE Xplore Digital Library erschienen ist.
DOI: 10.1109/DSAA.2017.27 - Freie Schlagwörter (DE)
- Zeitreihenanalyse, Vorhersage, groß angelegte Zeitreihendaten
- Freie Schlagwörter (EN)
- time series analysis, forecasting, large scale time series data
- Klassifikation (DDC)
- 005
- Verlag
- IEEE, New York, NY
- Förder- / Projektangaben
- Deutsche Forschungsgemeinschaft (DFG)
Flash-Forward Query Framework: Selbstjustierende, modellbasierte Verarbeitung deklarativer Prognoseanfragen in Data-Warehouse-SystemenID: 114523986 - Version / Begutachtungsstatus
- angenommene Version / Postprint / Autorenversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-821819
- Veröffentlichungsdatum Qucosa
- 18.01.2023
- Dokumenttyp
- Konferenzbeitrag
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis