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

被引：15

作者：

Kawagoe, K ^{[1
]}

Ueda, T ^{[1
]}

机构：

[1] Ritsumeikan Univ, Dept Comp Sci, Kusatsu, Shiga, Japan

来源：

NINTH INTERNATIONAL SYMPOSIUM ON TEMPORAL REPRESENTATION AND REASONING, PROCEEDINGS | 2002年

关键词：

D O I：

10.1109/TIME.2002.1027480

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：86 / 92

页数：7

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