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15th International Conference on Data Engineering (ICDE'99)
Efficient Time Series Matching by Wavelets
Sydney, Australia
March 23-March 26
ISBN: 0-7695-0071-4
Time series stored as feature vectors can be indexed by multi-dimensional index trees like R-Tree for fast retrieval. Due to the dimensionality curse problem, transformations are applied to time series to reduce the number of dimensions of the feature vectors. Different transformations like Discrete Fourier Transform (DFT), Discrete Wavelet Transform (DWT), Karhunen-Loeve (K-L) transform or Singular Value Decomposition (SVD) can be applied. While the use of DFT and K-L transform or SVD have been studied in the literature, to our knowledge, there is no in-depth study on the application of DWT. In this paper, we propose to use Haar Wavelet Transform for time series indexing. The major contributions are: (1) we show that Euclidean distance is preserved in the Haar transformed domain and no false dismissal will occur in range query, (2) we show that Haar transform can outperform DFT through experiments, (3) a new similarity model is suggested to accommodate vertical shift of time series, and (4) a two-phase method is proposed for efficient n-nearest neighbor query in time series databases.
Citation:
Kin-Pong Chan, Wai-Chee Fu, "Efficient Time Series Matching by Wavelets," icde, pp.126, 15th International Conference on Data Engineering (ICDE'99), 1999
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