DocumentCode :
3188852
Title :
Measuring time series similarity through large singular features revealed with wavelet transformation
Author :
Struzik, Zbigniew R. ; Siebes, Arno
Author_Institution :
CWI, Amsterdam, Netherlands
fYear :
1999
fDate :
1999
Firstpage :
162
Lastpage :
166
Abstract :
For the majority of data mining applications, there are no models of data which would facilitate the task of comparing records of time series. We propose a generic approach to comparing noise time series using the largest deviations from consistent statistical behaviour. For this purpose we use a powerful framework based on wavelet decomposition, which allows filtering polynomial bias, while capturing the essential singular behaviour. In addition, we are able to reveal scale-wise ranking of singular events including their scale free characteristic: the Holder exponent. We use a set of such characteristics to design a compact representation of the time series suitable for direct comparison, e.g. evaluation of the correlation product. We demonstrate that the distance between such representations closely corresponds with the subjective feeling of similarity between the time series. In order to test the validity of subjective criteria, we test the records of currency exchanges, finding convincing levels of (local) correlation
Keywords :
data mining; polynomials; time series; wavelet transforms; Holder exponent; correlation product; data mining; filtering polynomial bias; large singular features; statistical behaviour; time series similarity measurement; wavelet decomposition; wavelet transformation; Data mining; Electrical capacitance tomography; Filtering; Fluctuations; Inference algorithms; Measurement standards; Read only memory; Statistics; Testing; Time measurement;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Database and Expert Systems Applications, 1999. Proceedings. Tenth International Workshop on
Conference_Location :
Florence
Print_ISBN :
0-7695-0281-4
Type :
conf
DOI :
10.1109/DEXA.1999.795160
Filename :
795160
Link To Document :
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