Abstract:
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 use...Show MoreMetadata
Abstract:
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.
Date of Conference: 07-09 July 2002
Date Added to IEEE Xplore: 07 November 2002
Print ISBN:0-7695-1474-X
Print ISSN: 1530-1311