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A similarity search method of time series data with combination of Fourier and wavelet transforms

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2 Author(s)
Kawagoe, K. ; Comput. Sci. Dept., Ritsumeikan Univ., Shiga, Japan ; Ueda, T.

Time-series data, such as stock exchange rates and weather data, has widely been used in many fields. Similarity search of time-series data is important because it is useful for predicting data changes and searching for common sources. In this paper, we propose a new similarity search method of time-series data using both a discrete Fourier transform (DFT) and wavelet transform (WT). A method of reducing time-series indexing size, using a correlation coefficient, is also presented.

Published in:

Temporal Representation and Reasoning, 2002. TIME 2002. Proceedings.Ninth International Symposium on

Date of Conference:

2002