A Similarity Search Method of Time Series Data with Combination of Fourier and Wavelet Transforms

Authors:
Kyoji Kawagoe;Tomohiro Ueda
Affiliations:
-;-
Venue:
TIME '02 Proceedings of the Ninth International Symposium on Temporal Representation and Reasoning (TIME'02)
Year:
2002

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Abstract

Recently, 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 for searching for common sources. In this paper, we propose a new similarity search method of time-series data using both Discrete Fourier Transform (DFT) and Wavelet Transform (WT). A method of reducing time-series indexing size, using a correlation coefficient, is also presented.