Abstract
There are many processes under control. Nowadays all of them undergo through the automation procedure due to the rapid development of information technologies and computing devices. Because of the fact that many tasks of control deal with the elimination of situations, which can cause emergencies, automatic prediction of objective events in process streams becomes the popular problem decided by automation procedure. This work presents the new approach for intelligent analysis of processes represented by time series data. The main aim of our approach is prediction and detection of objective events. The idea of the technique is referred to the mapping of original time series into phase space and construction of the fuzzy prediction clusters of patterns in this phase space. Prediction stage consists of comparison of observed event with the prediction clusters according to the base of fuzzy rules.
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References
Povinelli, R.J.: Time series data mining: identifying temporal patterns for characterization and prediction of time series events. Doctoral dissertation, Faculty of the Graduate School, Marquette University (1999)
Sukhanov, A.V., Kovalev, S.M.: Anomaly detection based on Markov chain model with production rules. Programmnyye produkty i sistemy 3, 40–44 (2014)
Povinelli, R.J., Feng, X.: Data mining of multiple nonstationary time series. Artif. Neural Netw. Eng. 511–516 (1999)
Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthursamy, R.: Advances in Knowledge Discovery and Data Mining. AAAI Press, Menlo Park, Calif (1996)
Weiss, S.M., Indurkhya, N.: Predictive Data Mining: A Practical Guide. Morgan Kaufmann, San Francisco (1998)
Takens, F.: Detecting Strange Attractors in Turbulence. Springer, Berlin, pp. 366–381 (1981)
Feng, X., Huang, H.: A fuzzy-set-based reconstructed phase space method for identification of temporal patterns in complex time series. Knowl. Data Eng. IEEE Trans. 17(5), 601–613 (2005)
Hagan, M.T., Demuth, H.B., Beale, M.H.: Neural Network Design. Pws Pub, Boston, pp. 2–14 (1996)
Aydin, I., Karakose, M., Akin, E.: The prediction algorithm based on fuzzy logic using time series data mining method. World Acad. Sci. Eng. Technol. 51(27), 91–98 (2009)
Kantz, H., Schreiber, T.: Nonlinear Time Series Analysis, vol. 7. Cambridge University Press, Cambridge (2004)
Fraser, A.M., Swinney, H.L.: Independent coordinates for strange attractors from mutual information. Phys. Rev. A 33(2), 1134 (1986)
Kennel, M.B., Brown, R., Abarbanel, H.D.: Determining embedding dimension for phase-space reconstruction using a geometrical construction. Phys. Rev. A 45(6), 3403 (1992)
Rössler, O.E.: An equation for continuous chaos. Phys. Lett. A 57(5), 397–398 (1976)
Acknowledgments
This work was supported by the Russian Foundation for Basic Research (Grants No. 13-07-00183 A, 13-08-12151 ofi_m_RZHD), by SGS, VSB-Technical University of Ostrava, under the grant no. SP2014/110 and partially supported by Grant of SGS No. SP2015/151, VŠB—Technical University of Ostrava, Czech Republic.
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Kovalev, S., Sukhanov, A., Stýskala, V. (2016). Fuzzy Model Based Intelligent Prediction of Objective Events. In: Stýskala, V., Kolosov, D., Snášel, V., Karakeyev, T., Abraham, A. (eds) Intelligent Systems for Computer Modelling . Advances in Intelligent Systems and Computing, vol 423. Springer, Cham. https://doi.org/10.1007/978-3-319-27644-1_3
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DOI: https://doi.org/10.1007/978-3-319-27644-1_3
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