Abstract
The report focuses on the actual task of development of mathematical tool for data mining and nonlinear non-stationary processes forecasting. The proposed mathematical tool can use in solving the problems of data analysis of various nature for nonlinear non-stationary processes forecasting. The results of its application for prediction of such processes are present.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Baklan, I.V., Selin, Y.: Analysis of the behavior of economic time series using structural approaches. Bull. Kherson Natl. Tech. Univ. 2, 27–31 (2005). (in Russian)
Baklan, I.V., Selin, Y.: Structural approach to the analysis and prediction of behavior of time series. Bull. Kherson Natl. Tech. Univ. 5, 29–34 (2006). (in Russian)
Baklan, I.V., Savchenko, V.V., Selin, Y., Shulkevych, T.V.: Some aspects of nonlinear non-stationary processes forecasting. Syst. Terminol. 6(113), 31–42 (2017)
Bidyuk, P.I., Romanenko, V.D., Tymoshchuk, O.L.: Time series analysis. Tutorial, NTUU “KPI”, Kyiv (2013). (in Ukrainian)
Gurung, D., Chakraborty, U.K., Sharma, P.: An analysis of the intelligent predictive string search algorithm: a probabilistic approach. Int. J. Inf. Technol. Comput. Sci. (IJITCS) 9(2), 66–75 (2017). https://doi.org/10.5815/ijitcs.2017.02.08
Fu, К.S.: Sequential Methods in Pattern Recognition and Machine Learning. Academic Press, New York (1968)
Hu, Z., Bodyanskiy, Y.V., Tyshchenko, O.K., Boiko, O.O.: Adaptive forecasting of non-stationary nonlinear time series based on the evolving weighted neuro-neo-fuzzy-ANARX-model. Int. J. Inf. Technol. Comput. Sci. (IJITCS) 8(10), 1–10 (2016). https://doi.org/10.5815/ijitcs.2016.10.01
Khashei, M., Montazeri, M.A., Bijari, M.: Comparison of four interval ARIMA-base time series methods for exchange rate forecasting. Int. J. Math. Sci. Comput. (IJMSC) 1(1), 21–34 (2015). https://doi.org/10.5815/ijmsc.2015.01.03
Mezzi, M., Benblidia, N.: Study of context modelling criteria in information retrieval. Int. J. Inf. Technol. Comput. Sci. (IJITCS) 9(3), 28–39 (2017). https://doi.org/10.5815/ijitcs.2017.03.04
Mishra, N., Soni, H.K., Sharma, S., Upadhyay, A.K.: Development and analysis of artificial neural network models for rainfall prediction by using time-series data. Int. J. Intell. Syst. Appl. (IJISA) 10(1), 16–23 (2018). https://doi.org/10.5815/ijisa.2018.01.03
Montgomery, D.C., Jennings, C.L., Kulahci, M.: Introduction to Time Series Analysis and Forecasting, 2nd edn (2015)
Ali, S.K., Azeez, Z.N., Ouda, A.A.H.: A new clustering algorithm for face classification. Int. J. Inf. Technol. Comput. Sci. (IJITCS) 8(6), 1–8 (2016). https://doi.org/10.5815/ijitcs.2016.06.01
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
Shulkevich, T., Selin, Y., Savchenko, V. (2020). Data Mining and Nonlinear Non-stationary Processes Forecasting by Using Linguistic Modeling Method. In: Hu, Z., Petoukhov, S., Dychka, I., He, M. (eds) Advances in Computer Science for Engineering and Education II. ICCSEEA 2019. Advances in Intelligent Systems and Computing, vol 938. Springer, Cham. https://doi.org/10.1007/978-3-030-16621-2_38
Download citation
DOI: https://doi.org/10.1007/978-3-030-16621-2_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-16620-5
Online ISBN: 978-3-030-16621-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)