Poster : Visual Prediction of Time Series

Publikationstyp: Beitrag zu einer Konferenz
URI (zitierfähiger Link): http://nbn-resolving.de/urn:nbn:de:bsz:352-192027
Autor/innen: Hao, Ming; Janetzko, Halldor; Sharma, Ratnesh; Dayal, Umeshwar; Keim, Daniel; Castellanos, Malu
Erscheinungsjahr: 2009
Konferenz: 2009 IEEE Symposium on Visual Analytics Science and Technology, 12. Okt 2009 - 13. Okt 2009, Atlantic City, NJ, USA
Erschienen in: 2009 IEEE Symposium on Visual Analytics Science and Technology. - IEEE, 2009. - S. 229-230. - ISBN 978-1-4244-5283-5
DOI (zitierfähiger Link): https://dx.doi.org/10.1109/VAST.2009.5333420
Zusammenfassung:
Many well-known time series prediction methods have been used daily by analysts making decisions. To reach a good prediction, we introduce several new visual analysis techniques of smoothing, multi-scaling, and weighted average with the involvement of human expert knowledge. We combine them into a well-fitted method to perform prediction. We have applied this approach to predict resource consumption in data center for next day planning.
Fachgebiet (DDC): 004 Informatik
Link zur Lizenz: Nutzungsbedingungen
Universitätsbibliographie: Ja

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HAO, Ming, Halldor JANETZKO, Ratnesh SHARMA, Umeshwar DAYAL, Daniel KEIM, Malu CASTELLANOS, 2009. Poster : Visual Prediction of Time Series. 2009 IEEE Symposium on Visual Analytics Science and Technology. Atlantic City, NJ, USA, 12. Okt 2009 - 13. Okt 2009. In: 2009 IEEE Symposium on Visual Analytics Science and Technology. IEEE, pp. 229-230. ISBN 978-1-4244-5283-5. Available under: doi: 10.1109/VAST.2009.5333420

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