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

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.