Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A Unifying Framework for Detecting Outliers and Change Points from Time Series
IEEE Transactions on Knowledge and Data Engineering
k-Means Has Polynomial Smoothed Complexity
FOCS '09 Proceedings of the 2009 50th Annual IEEE Symposium on Foundations of Computer Science
Efficient Periodicity Mining in Time Series Databases Using Suffix Trees
IEEE Transactions on Knowledge and Data Engineering
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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.