TIME SERIES FORECASTING BY THE ARIMA METHOD
DOI:
https://doi.org/10.37943/HFCH4395Keywords:
time series, network traffic, data analysis, forecasting, ARIMAAbstract
The variety of communication services and the growing number of different sensors with the appearance of IoT (Internet of Things) technology generate significantly different types of network traffic. This implies that the structure of network traffic will be heterogeneous, which requires deep analysis to find the internal features underlying the data. A common model for analyzing the processes of a multiservice network is a model based on time series.
Numerous empirical data studies indicate that the packet intensity time series do not belong to the general aggregates of a normal distribution.
The problem of predicting network traffic is still relevant due to managing information that flows into a heterogeneous network.
In this work, the authors studied the time series for stationarity in order to select an appropriate forecasting model. A visual assessment of the series assumed non-stationarity. The Augmented Dickey-Fuller Test is applied, and the measured network traffic is predicted using the ARIMA (Auto-Regressive Integrated Moving Average) statistical method. Results were obtained using the Econometric Modeler Matlab (R2021b) application. The results of the autocorrelation function (ACF) and partial ACF are analyzed, with the help of which the ARIMA model is optimized. As a result of the study, a software algorithm for the ARIMA (0,2,1) model was developed.
References
Serikov, T., Zhetpisbayeva, A., Аkhmediyarova, А., Mirzakulova, S., Kismanova, A., Tolegenova, A., & Wójcik, W. (2021). City backbone network traffic forecasting. International Journal of Electronics and Telecommunications, 67 (3), 319–324. https://doi.org/ 10.24425/ijet.2021.135983
Serikov, T., Zhetpisbayeva, A., Mirzakulova, S., Zhetpisbayev, K., Ibraeva, Z., Sobolevа, L., Tolegenova, A., & Zhumazhanov, B. (2021). Application of the NARX neural network for predicting a one-dimensional time series. Eastern-European Journal of Enterprise Technologies, 5 (4), 12–19. https://doi.org/10.15587/1729-4061.2021.242442
Al-Saati, N. H., Omran, I. I., Salman, A. A., Al-Saati, Z., & Hashim, K. S. (2021). Statistical modeling of monthly streamflow using time series and artificial neural network models: Hindiya Barrage as a case study. Water Practice and Technology, 16(2), 681-691. https://doi.org/10.2166/wpt.2021.012
Khedkar, S. P., Canessane, R. A., & Najafi, M. L. (2021). Prediction of traffic generated by IoT devices using statistical learning time series algorithms. Wireless Communications and Mobile Computing, 1–12. https://doi.org/10.1155/2021/5366222
Weerakody, P. B., Wong, K. W., & Wang, G., E. (2021). A review of irregular time series data handling with gated recurrent neural networks. Neurocomputing, 441, 161–178. https://doi.org/10.1016/j.neucom.2021.02.046
Sovetov, B. Y., Tatarnikova, T. M., & Tsekhanovskiy, V. V. (2020) Avtoregressionnyye modeli prognozirovaniya setevogo trafika. Materialy konferentsii «Informatsionnyye tekhnologii v upravlenii». [Autoregressive models for predicting network traffic. Proceedings of the conference "Information technologies in management"], Saint-Petersburg Electrotechnical University "LETI" named after V.I. Ulyanov (Lenin).
Rizkya, I., Syahputri, K., Sari, R. M., Siregar, I., & Utaminingrum, J. (2019, August). Autoregressive Integrated Moving Average (ARIMA) Model of Forecast Demand in Distribution Centre. In IOP Conference Series: Materials Science and Engineering (Vol. 598, No. 1, p. 012071). IOP Publishing. https://doi.org/10.1088/1757-899X/598/1/012071
Joshi, M., & Hadi, T. H. (2015). A review of network traffic analysis and prediction techniques. arXiv preprint arXiv:1507.05722.
Rutka, G. (2008). Network traffic prediction using ARIMA and neural networks models. Elektronika ir Elektrotechnika, 84(4), 53-58.
Brockwell, P. J., & Davis, R. A. (Eds.). (2002). Introduction to time series and forecasting. New York, NY: Springer New York. https://doi.org/10.1007/0-387-21657-X_8
Brownlee, J. (2017). How to Create an ARIMA Model for Time Series Forecasting in Python. Machine Learning Mastery. https://machinelearningmastery.com/arima-for-time-series-forecasting-with-python/
Maltseva, K. (2018). ARIMA: making predictions based on history. Foresight. https://www.fsight.ru/blog/arima-stroim-prognoz-na-osnove-istorii/
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Articles are open access under the Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish a manuscript in this journal agree to the following terms:
- The authors reserve the right to authorship of their work and transfer to the journal the right of first publication under the terms of the Creative Commons Attribution License, which allows others to freely distribute the published work with a mandatory link to the the original work and the first publication of the work in this journal.
- Authors have the right to conclude independent additional agreements that relate to the non-exclusive distribution of the work in the form in which it was published by this journal (for example, to post the work in the electronic repository of the institution or publish as part of a monograph), providing the link to the first publication of the work in this journal.
- Other terms stated in the Copyright Agreement.