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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Granular-Based Partial Periodic Pattern Discovery over Time Series Data
verfasst von : Aibao Luo, Xiuyi Jia, Lin Shang, Yang Gao, Yubin Yang
Erschienen in: Rough Sets and Knowledge Technology
Verlag: Springer Berlin Heidelberg
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Periodicity analysis of the time series is getting more and more significant. There are many contributions for periodic pattern discovery, however, few laid emphasis on the further usage. In the paper, we propose a granular-based partial periodic pattern detecting method over time series data. This method can detect all patterns of every possible periodicity without any prior knowledge of the data sets, by setting different granularity and minimum support threshold. The results that it learned can be used in outlier or change point detection in time series data analysis. The experiment results show its effectiveness.