Efficient similarity queries over time series data using wavelets

grantor: University of TorontoWe consider the use of wavelet transformations as a dimensionality reduction technique to permit efficient similarity search over high dimensional time series data. While numerous transformations have been proposed and studied, the only wavelet that has been shown to be effective for this application is the Haar wavelet. In this thesis, we prove that a large class of wavelet transformations (not only orthonormal wavelets but also bi-orthonormal wavelets) can be used to support similarity search. We present a detailed performance study of the effects of using different wavelets on the performance of similarity search for time series data. We include several wavelets that outperform both the Haar wavelet and the best known non-wavelet transformations for this application. We conducted a study including different types of time series data. We define the set of indexable classes of data and we show the performance of our proposed wavelet transformations.M.Sc