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The work considers a one-dimensional time series protocol packet intensity, measured on the city backbone network. The intensity of the series is uneven. Scattering diagrams are constructed. The Dickie Fuller test and Kwiatkowski-Phillips Perron-Shin-Schmitt test were applied to determine the initial series to the class of stationary or non-stationary series. Both tests confirmed the involvement of the original series in the class of differential stationary. Based on the Dickie Fuller test and Private autocorrelation function graphs, the Integrated Moving Average Autoregression Model model is created. The results of forecasting network traffic showed the adequacy of the selected model.
Rocznik
Tom
Strony
319--324
Opis fizyczny
Bibliogr. 10 poz., tab., wykr.
Twórcy
autor
- S. Seifullin Kazakh Agro Technical University, Nur-Sultan, Kazakhstan
autor
- S. Seifullin Kazakh Agro Technical University, Nur-Sultan, Kazakhstan
autor
- Institute of Information and Computational Technologies, Almaty, Kazakhstan
autor
- Turan University, Almaty, Kazakhstan
autor
- S. Seifullin Kazakh Agro Technical University, Nur-Sultan, Kazakhstan
autor
- S. Seifullin Kazakh Agro Technical University, Nur-Sultan, Kazakhstan
autor
- Lublin University of Technology, Poland
Bibliografia
- [1] V. S. Maraev, “Time series visualization Tools in space research. Volume 1”, Research of science city, vol. 4, no. 22, 2017
- [2] G. G. Kantorovich, “Analysis of temporal rows. Lecture and methodical materials”, Economic Journal of the Higher School of Economics, no. 3, 2002, pp. 379-701.
- [3] M. S. Vershinina, “Analysis of assumptions about the stationarity of some temporal series”, Collection of the all-Russian conference on mathematics with international participation "IAC-2018", Barnaul: AltSU University, 2018, pp. 172-176.
- [4] R. M. De Jong, C. Amsler, and P. Schmidt, “A robust version of the KPSS test, based on indicators”, J. Econometrics, vol. 137, no. 2, 2007, pp. 311–333.
- [5] W. Wojcik, T. Bieganski, A. Kotyra, and A. Smolarz, "Application of forecasting algorithms in the optical fiber coal dust burner monitoring system", Proc. SPIE 3189, Technology and Applications of Light Guides, (5 August 1997); https://doi.org/10.1117/12.285618
- [6] K. O. Kizbikenov, “Prognostication and temporary series: textbook by K. O. Kizbikenov”, Barnaul:AltSPU, 2017.
- [7] V. S. Korolyuk, N. I. Portenko, A. V. Skorokhod, A. F. Turbin (eds.) “Handbook of probability theory and mathematical statistics”, Moscow: Nauka, 2005.
- [8] G. Box, G. Jenkins, “Time Series Analysis: Forecasting and Control,” San Francisco: Holden-Day, 1970.
- [9] I. Rizkya, K. Syahputri, R. M. Sari, I. Siregar and J. Utaminingrum, “Autoregressive Integrated Moving Average (ARIMA) Model of Forecast Demand in Distribution Centre,” Department of Industrial Engineering, Faculty of Engineering, Universitas Sumatera Utara in IOP Conf. Series: Materials Science and Engineering 598, 2019, 012071.
- [10] N. Albanbay, B. Medetov, M. A. Zaks, “Statistics of Lifetimes for Transient Bursting States in Coupled Noisy Excitable Systems,” Journal of Computational and Nonlinear Dynamics. vol. 15, no. 12, 2020, https://doi.org/10.1115/1.4047867
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5fecb607-40cc-4507-8830-0e72c971a573