Change Detection in Time Series Data Using Wavelet Footprints

In this paper, we propose a novel approach to address the problem of change detection in time series data. Our approach is based on wavelet footprints proposed originally by the signal processing community for signal compression. We, however, exploit the properties of footprints to capture discontinuities in a signal. We show that transforming data using footprints generates nonzero coefficients only at the change points. Exploiting this property, we propose a change detection query processing scheme which employs footprint-transformed data to identify change points, their amplitudes, and degrees of change efficiently and accurately. Our analytical and empirical results show that our approach outperforms the best known change detection approach in terms of both performance and accuracy. Furthermore, unlike the state of the art approaches, our query response time is independent of the number of change points and the user-defined change threshold.