Autoregressive and Arima Pro-integrated Moving Average Models for Network Traffic Forecasting

Alexandra Grebenshchikova; Vasily Elagin; Artem Volkov; Ibrahim A. Elgendy

doi:10.1007/978-3-031-80853-1_5

Autoregressive and Arima Pro-integrated Moving Average Models for Network Traffic Forecasting

Alexandra Grebenshchikova, Vasily Elagin, Artem Volkov, Ibrahim A. Elgendy^*

^*Corresponding author for this work

Interdisciplinary Research Center for Finance and Digital Economy

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The predictability of network traffic is critical in optimizing future network architectures, where an accurate traffic prediction model ensures high-quality service. This research focuses on forecasting network traffic using ARIMA models, particularly addressing significant traffic characteristics such as long-range dependence (LRD), self-similarity, and multifractality across various time scales. Utilizing Internet of Things (IoT) data, this study developed and evaluated multiple ARIMA models based on performance metrics including the Akaike Information Criterion (AIC), Schwarz Bayesian Criterion (SBC), maximum likelihood, and standard error. The research highlights the model’s efficacy in capturing linear dependencies within network traffic, while also acknowledging the limitations of ARIMA models in handling data burst characteristics. Consequently, it suggests the potential integration with GARCH models to improve prediction accuracy by incorporating time-varying volatility.

Original language	English
Title of host publication	Distributed Computer and Communication Networks - 27th International Conference, DCCN 2024, Revised Selected Papers
Editors	Vladimir M. Vishnevsky, Konstantin E. Samouylov, Dmitry V. Kozyrev
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	54-68
Number of pages	15
ISBN (Print)	9783031808524
DOIs	https://doi.org/10.1007/978-3-031-80853-1_5
State	Published - 2025
Event	27th International Conference on Distributed Computer and Communication Networks, DCCN 2024 - Moscow, Russian Federation Duration: 23 Sep 2024 → 27 Sep 2024

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	15460 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	27th International Conference on Distributed Computer and Communication Networks, DCCN 2024
Country/Territory	Russian Federation
City	Moscow
Period	23/09/24 → 27/09/24

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Keywords

ARIMA mode
Linear time series
Multifractality
Non-linear GARCH model
Self-similarity
Traffic prediction model

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-80853-1_5

Cite this

Grebenshchikova, A., Elagin, V., Volkov, A., & Elgendy, I. A. (2025). Autoregressive and Arima Pro-integrated Moving Average Models for Network Traffic Forecasting. In V. M. Vishnevsky, K. E. Samouylov, & D. V. Kozyrev (Eds.), Distributed Computer and Communication Networks - 27th International Conference, DCCN 2024, Revised Selected Papers (pp. 54-68). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 15460 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-80853-1_5

Grebenshchikova, Alexandra ; Elagin, Vasily ; Volkov, Artem et al. / Autoregressive and Arima Pro-integrated Moving Average Models for Network Traffic Forecasting. Distributed Computer and Communication Networks - 27th International Conference, DCCN 2024, Revised Selected Papers. editor / Vladimir M. Vishnevsky ; Konstantin E. Samouylov ; Dmitry V. Kozyrev. Springer Science and Business Media Deutschland GmbH, 2025. pp. 54-68 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The predictability of network traffic is critical in optimizing future network architectures, where an accurate traffic prediction model ensures high-quality service. This research focuses on forecasting network traffic using ARIMA models, particularly addressing significant traffic characteristics such as long-range dependence (LRD), self-similarity, and multifractality across various time scales. Utilizing Internet of Things (IoT) data, this study developed and evaluated multiple ARIMA models based on performance metrics including the Akaike Information Criterion (AIC), Schwarz Bayesian Criterion (SBC), maximum likelihood, and standard error. The research highlights the model{\textquoteright}s efficacy in capturing linear dependencies within network traffic, while also acknowledging the limitations of ARIMA models in handling data burst characteristics. Consequently, it suggests the potential integration with GARCH models to improve prediction accuracy by incorporating time-varying volatility.",

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author = "Alexandra Grebenshchikova and Vasily Elagin and Artem Volkov and Elgendy, \{Ibrahim A.\}",

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Grebenshchikova, A, Elagin, V, Volkov, A & Elgendy, IA 2025, Autoregressive and Arima Pro-integrated Moving Average Models for Network Traffic Forecasting. in VM Vishnevsky, KE Samouylov & DV Kozyrev (eds), Distributed Computer and Communication Networks - 27th International Conference, DCCN 2024, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 15460 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 54-68, 27th International Conference on Distributed Computer and Communication Networks, DCCN 2024, Moscow, Russian Federation, 23/09/24. https://doi.org/10.1007/978-3-031-80853-1_5

Autoregressive and Arima Pro-integrated Moving Average Models for Network Traffic Forecasting. / Grebenshchikova, Alexandra; Elagin, Vasily; Volkov, Artem et al.
Distributed Computer and Communication Networks - 27th International Conference, DCCN 2024, Revised Selected Papers. ed. / Vladimir M. Vishnevsky; Konstantin E. Samouylov; Dmitry V. Kozyrev. Springer Science and Business Media Deutschland GmbH, 2025. p. 54-68 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 15460 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Grebenshchikova A, Elagin V, Volkov A, Elgendy IA. Autoregressive and Arima Pro-integrated Moving Average Models for Network Traffic Forecasting. In Vishnevsky VM, Samouylov KE, Kozyrev DV, editors, Distributed Computer and Communication Networks - 27th International Conference, DCCN 2024, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2025. p. 54-68. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-80853-1_5