A survey on wavelet applications in data mining
ACM SIGKDD Explorations Newsletter
Assisting decision making in the event-driven enterprise using wavelets
Decision Support Systems
Efficient discovery of unusual patterns in time series
New Generation Computing
Discrete wavelet transform-based time series analysis and mining
ACM Computing Surveys (CSUR)
A novel clustering method on time series data
Expert Systems with Applications: An International Journal
ACM Computing Surveys (CSUR)
Ubiquitous intelligent information push-delivery for personalized content recommendation
UIC'07 Proceedings of the 4th international conference on Ubiquitous Intelligence and Computing
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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 behavior. For this purpose we use a powerful framework based on wavelet decomposition, which allows filtering polynomial bias, while capturing the essential singular behavior. In addition, we are able to reveal scale-wise ranking of singular events including their scale free characteristic: the Hölder 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.