Journal of Machine and Computing

Multilayer Seasonal Autoregressive Integrated Moving Average Models for Complex Network Traffic Analysis

Journal of Machine and Computing

Received On : 16 April 2023

Revised On : 19 July 2023

Accepted On : 06 December 2023

Published On : 05 January 2024

Volume 04, Issue 01

Pages : 238-249


The ever-increasing amount of network traffic generated by various devices and applications has made it crucial to have efficient methods for analyzing and managing network traffic. Traditional approaches, such as statistical modeling, have yet to be proven enough due to network traffic's complex nature and dynamic characteristics. Recent research has shown the effectiveness of complex network analysis techniques for understanding network traffic patterns. This paper proposes multilayer seasonal autoregressive integrated moving average models for analyzing and predicting network traffic. This approach considers the seasonal patterns and interdependencies between different layers of network traffic, allowing for a more accurate and comprehensive representation of the data. The Multilayer Seasonal Autoregressive Integrated Moving Average (MSARIMA) model consists of multiple layers, each representing a different aspect of network traffic, such as time of day, day of week, or type of traffic. Each layer is modeled separately using SARIMA, a popular time series forecasting technique. The models for different layers are combined to capture the overall behavior of network traffic. The proposed approach has several benefits over traditional statistical approaches. It can capture network traffic's complex and dynamic nature, including short-term and long-term seasonal patterns. It also allows for the detection of anomalies and the prediction of future traffic patterns with high accuracy.


Network, Traffic, Statistical Modeling, Dynamic Characteristics, Seasonal Patterns.

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Prathipa Ravanappan, Maragatharajan M, Rashika Tiwari, Srihari T and Lavanya K, “Multilayer Seasonal Autoregressive Integrated Moving Average Models for Complex Network Traffic Analysis”, Journal of Machine and Computing, pp. 238-249, January 2024. doi: 10.53759/7669/jmc202404023.


© 2024 Prathipa Ravanappan, Maragatharajan M, Rashika Tiwari, Srihari T and Lavanya K. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.