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 well-fitted 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.