A Visual Analytics Approach for Peak-Preserving Prediction of Large Seasonal Time Series
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Date
2011
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and Blackwell Publishing Ltd.
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.
Description
@article{:10.1111/j.1467-8659.2011.01918.x,
journal = {Computer Graphics Forum},
title = {{A Visual Analytics Approach for Peak-Preserving Prediction of Large Seasonal Time Series}},
author = {Hao, M. C. and Janetzko, H. and Mittelstädt, S. and Hill, W. and Dayal, U. and Keim, D. A. and Marwah, M. and Sharma, R. K.},
year = {2011},
publisher = {The Eurographics Association and Blackwell Publishing Ltd.},
ISSN = {1467-8659},
DOI = {/10.1111/j.1467-8659.2011.01918.x}
}