Abstract
Period detection is one of the most important tasks of time series analysis. Different period detection methods have been proposed for different kind of time series. This paper proposes a period detection method for numerical time series, which is a generalization of the period detection methods for event time series and symbol time series. In the proposed method, fuzzy partition is first carried out on the value domain to build the events, with these events the original time series is then transformed into a multi-event time series; and from this multi-event time series a group of single-event time series are finally built. Period detection is done for each single-event time series. From these detections, the periodicity of the given time series can be asserted. This method can give not only the global period of a time series if the period exists, but also the partial period(s) if the period(s) exist(s). Thus, the proposed period detection method for numerical time series has wider application areas than that for even time series. Experiments on both the synthetic dataset and real dataset showed the effectiveness of the proposed method in this paper.
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Acknowledgement
This work is supported by the National Natural Science Foundation of China (No.11971065, No. 11571001, No. 11701338).
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Xu, J., Yu, F., Liu, Y., Wang, X. (2020). Fuzzy Partition Based Period Detection Method for Numerical Time Series. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1075. Springer, Cham. https://doi.org/10.1007/978-3-030-32591-6_27
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DOI: https://doi.org/10.1007/978-3-030-32591-6_27
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