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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Elfeky, M.G., Aref, W.G., Elmagarmid, A.K.: Periodicity detection in time series databases. IEEE Trans. Knowl. Data Eng. 17(7), 875–887 (2005)
Nishi, M.A., et al.: Effective periodic pattern mining in time series databases. Expert Syst. Appl. 40(8), 3015–3027 (2013)
Li, Z., Wang, J., Han, J.: EPeriodicity: mining event periodicity from incomplete observations. IEEE Trans. Knowl. Data Eng. 27(5), 1219–1232 (2015)
Hu, Y., Chen, N.: Voiced/unvoiced classification and pitch period detection algorithm based on wavelet transform. J. Electron. Inf. Technol. 30, 353–356 (2008)
Guan, T., Wang, K.R., Zhang, S.P.: A robust periodicity mining method from incomplete and noisy observations based on relative entropy. Int. J. Mach. Learn. Cybern. 8(1), 283–293 (2017)
Durrett, R.: Probability: Theory and Examples, no. 5. vol. 39, pp. 320–353. Cambridge University Press, Cambridge (2005)
Guo, L.: Periodic study of time series data. University of Electronic Science and Technology (2013)
Chen, X.Y., Ye, D.Y., Hu, X.L.: Entropy-based symbolic representation for time series classification. In: International Conference on Fuzzy Systems and Knowledge Discovery, pp. 754–760. IEEE Computer Society (2007)
Jeng, Y.N., Cheng, Y.C.: A short time fourier transform for time series data. J. Aeronaut. Aviat. 39(2), 117–127 (2007)
Luo, C.Z.: Introduction to Fuzzy Sets. Beijing Normal University Press, Beijing (2007)
Acknowledgement
This work is supported by the National Natural Science Foundation of China (No.11971065, No. 11571001, No. 11701338).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-32591-6_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32590-9
Online ISBN: 978-3-030-32591-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)