DocumentCode
2391216
Title
Poster: Visual prediction of time series
Author
Hao, Ming C. ; Janetzko, Halldór ; Sharma, Ratnesh K. ; Dayal, Umeshwar ; Keim, Daniel A. ; Castellanos, Malu
Author_Institution
Hewlett-Packard Labs., Palo Alto, CA, USA
fYear
2009
fDate
12-13 Oct. 2009
Firstpage
229
Lastpage
230
Abstract
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.
Keywords
data visualisation; smoothing methods; time series; multiscaling technique; smoothing technique; time series prediction methods; visual analysis techniques; weighted average technique; Energy consumption; History; Humans; Marketing and sales; Pattern analysis; Pipelines; Prediction methods; Smoothing methods; Supply chains; Time series analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Visual Analytics Science and Technology, 2009. VAST 2009. IEEE Symposium on
Conference_Location
Atlantic City, NJ
Print_ISBN
978-1-4244-5283-5
Type
conf
DOI
10.1109/VAST.2009.5333420
Filename
5333420
Link To Document