Name
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 with success to predict resource consumption in data centers. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/VAST.2009.5333420 | IEEE VAST |
Keywords | Field | DocType |
visualization,visual analysis,time series prediction,time series analysis,time series,data models,data center,data visualisation,predictive models | Resource consumption,Data mining,Time series,Data modeling,Data visualization,Computer science,Visualization,Smoothing,Data center,Weighted arithmetic mean | Conference |
Citations | PageRank | References |
2 | 0.47 | 2 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming C. Hao | 1 | 2 | 0.47 |
| Halldor Janetzko | 2 | 312 | 20.69 |
| Ratnesh K. Sharma | 3 | 483 | 53.37 |
| Umeshwar Dayal | 4 | 8452 | 2538.92 |
| Daniel A. Keim | 5 | 7704 | 1141.60 |
| Malú Castellanos | 6 | 351 | 74.33 |