A Visual Analytics Approach for Peak-Preserving Prediction of Large Seasonal Time Series

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dc.contributor.authorHao, M. C.en_US
dc.contributor.authorJanetzko, H.en_US
dc.contributor.authorMittelstädt, S.en_US
dc.contributor.authorHill, W.en_US
dc.contributor.authorDayal, U.en_US
dc.contributor.authorKeim, D. A.en_US
dc.contributor.authorMarwah, M.en_US
dc.contributor.authorSharma, R. K.en_US
dc.contributor.editorH. Hauser, H. Pfister, and J. J. van Wijken_US
dc.date.accessioned2014-02-21T20:23:22Z
dc.date.available2014-02-21T20:23:22Z
dc.date.issued2011en_US
dc.description.abstractTime 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.en_US
dc.description.number3en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume30en_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/j.1467-8659.2011.01918.xen_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltd.en_US
dc.subjectCR Categories and Subject Descriptorsen_US
dc.subjectI.3.3 [Computer Graphics]en_US
dc.subjectPicture/Image Generation Display Algorithmsen_US
dc.subjectH.5.0 [Information Systems]en_US
dc.subjectInformation Interfaces and Presentation Generalen_US
dc.titleA Visual Analytics Approach for Peak-Preserving Prediction of Large Seasonal Time Seriesen_US
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