Abstract
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.
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References
Brockwell, P.J., Davis, R.A.: Introduction to Time Series and Forecasting, 2nd edn. Springer, New York (2002)
Han, J., Kamber, M.: Data Mining Concepts and Techniques, 2nd edn. China Machine Press, Beijing (2002)
Han, J., Dong, G., Yin, Y.: Efficient Mining of Partial Periodic Patterns in Time Series Databases. In: 15th International Conference on Data Engineering, pp. 106–115 (1999)
Laxman, S., Sastry, P.S.: A survey of temporal data mining. Sadhana 31, 173–198 (2006)
Berberidis, C., Aref, W.G., Atallah, M., Vlahavas, I., Elmagarmid, A.K.: Multiple and Partial Periodicity Mining in Time Series Databases. In: 15th European Conference on Artificial Intelligence, pp. 370–374 (2002)
Han, J., Gong, W., Yin, Y.: Mining segment-wise periodic patterns in time-related databases. In: 4th Knowledge Discovery and Data Mining, pp. 214–218 (1998)
Takeuchi, J., Yamanishi, K.: A unifying framework for detecting outliers and change points from time series. IEEE Transactions on Knowledge and Data Engineering 18(4), 482–492 (2006)
Rasheeed, F., Alshalalfa, M., Alhajj, R.: Efficient Periodicity Mining in Time Series Databases Using Suffix Trees. IEEE Transactions on Knowledge and Data Engineering 23(1), 79–94 (2011)
David, A., Bodo, M., Heiko, R.: K-means has polynomial smoothed complexity. In: 50th IEEE Symposium on Foundations of Computer Science, pp. 405–414 (2009)
Tsay, R.S.: Analysis of Financial Time Series. Wiley-Interscience, Hoboken (2002)
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Luo, A., Jia, X., Shang, L., Gao, Y., Yang, Y. (2011). Granular-Based Partial Periodic Pattern Discovery over Time Series Data. In: Yao, J., Ramanna, S., Wang, G., Suraj, Z. (eds) Rough Sets and Knowledge Technology. RSKT 2011. Lecture Notes in Computer Science(), vol 6954. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24425-4_88
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DOI: https://doi.org/10.1007/978-3-642-24425-4_88
Publisher Name: Springer, Berlin, Heidelberg
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