Name
Title | ||
|---|---|---|
A visual analytics approach for peak-preserving prediction of large seasonal time series |
Abstract | ||
|---|---|---|
Time series prediction methods are used on a daily basis by analysts for making important decisions. Most of these methods use some variant of moving averages to reduce the number of data points before prediction. However, to reach a good prediction in certain applications (e.g., power consumption time series in data centers) it is important to preserve peaks and their patterns. In this paper, we introduce automated peak-preserving smoothing and prediction algorithms, enabling a reliable long term prediction for seasonal data, and combine them with an advanced visual interface: (1) using high resolution cell-based time series to explore seasonal patterns, (2) adding new visual interaction techniques (multi-scaling, slider, and brushing & linking) to incorporate human expert knowledge, and (3) providing both new visual accuracy color indicators for validating the predicted results and certainty bands communicating the uncertainty of the prediction. We have integrated these techniques into a wellfitted solution to support the prediction process, and applied and evaluated the approach to predict both power consumption and server utilization in data centers with 70-80% accuracy. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1111/j.1467-8659.2011.01918.x | Comput. Graph. Forum |
Keywords | Field | DocType |
reliable long term prediction,data center,visual analytics approach,peak-preserving prediction,data point,large seasonal time series,cell-based time series,prediction process,advanced visual interface,good prediction,time series prediction method,prediction algorithm,seasonal data,general | Data point,Data mining,Time series,Computer vision,Long-term prediction,Computer science,Visual analytics,Prediction algorithms,Smoothing,Artificial intelligence,Moving average,Power consumption | Journal |
Volume | Issue | ISSN |
30 | 3 | 0167-7055 |
Citations | PageRank | References |
15 | 0.95 | 8 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. C. Hao | 1 | 15 | 1.29 |
| Halldor Janetzko | 2 | 312 | 20.69 |
| S. Mittelstädt | 3 | 19 | 1.69 |
| W. Hill | 4 | 15 | 0.95 |
| Umeshwar Dayal | 5 | 8452 | 2538.92 |
| Daniel A. Keim | 6 | 7704 | 1141.60 |
| M. Marwah | 7 | 76 | 3.40 |
| R. K. Sharma | 8 | 15 | 0.95 |