- Titel
- Efficient Forecasting for Hierarchical Time Series
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-803666
- Konferenz
- CIKM'13: 22nd ACM International Conference on Information and Knowledge Management. San Francisco, 27. Oktober - 01. November 2013
- Quellenangabe
- CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge ManagementHerausgeber: Qi He
Herausgeber: Arun Iyengar
Herausgeber: Wolfgang Nejdl
Herausgeber: Jian Pei
Herausgeber: Rajeev Rastogi
Erscheinungsort: New York
Verlag: ACM
Erscheinungsjahr: 2013
Seiten: 2399-2404
ISBN: 978-1-4503-2263-8 - Erstveröffentlichung
- 2013
- Abstract (EN)
- Forecasting is used as the basis for business planning in many application areas such as energy, sales and traffic management. Time series data used in these areas is often hierarchically organized and thus, aggregated along the hierarchy levels based on their dimensional features. Calculating forecasts in these environments is very time consuming, due to ensuring forecasting consistency between hierarchy levels. To increase the forecasting efficiency for hierarchically organized time series, we introduce a novel forecasting approach that takes advantage of the hierarchical organization. There, we reuse the forecast models maintained on the lowest level of the hierarchy to almost instantly create already estimated forecast models on higher hierarchical levels. In addition, we define a hierarchical communication framework, increasing the communication flexibility and efficiency. Our experiments show significant runtime improvements for creating a forecast model at higher hierarchical levels, while still providing a very high accuracy.
- Andere Ausgabe
- Link zum Artikel, der zuerst in der ACM Digital Library erschienen ist.
DOI: 10.1145/2505515.2505622 - Freie Schlagwörter (DE)
- Prognose, Hierarchien, Zeitreihen, Optimierung
- Freie Schlagwörter (EN)
- Forecasting, Hierarchies, Time Series, Optimization
- Klassifikation (DDC)
- 004
- Verlag
- ACM, New York
- Förder- / Projektangaben
- European Commission (EC)
FP7 | SP1 | ICT
Micro-Request-Based Aggregation, Forecasting and Scheduling of Energy Demand, Supply and Distribution
(MIRABEL)
ID: 248195 - Version / Begutachtungsstatus
- angenommene Version / Postprint / Autorenversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-803666
- Veröffentlichungsdatum Qucosa
- 11.08.2022
- Dokumenttyp
- Konferenzbeitrag
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
