• 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