Towards Forecasting Low Network Traffic for Software Patch Downloads: An ARMA Model Forecast Using CRONOS

Tan, Ian K. T.; Hoong, Poo Kuan; Keong, Chee Yik

doi:10.1109/iccnt.2010.35

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…In Hoong [28] and Tan et al [29], prediction models for network traffic demand are developed by applying the Autoregressive Moving Average (ARMA) time-series modeling technique. Such model is composed of two main terms: an AutoRegressive component (AR), which is the sum of past observations with a white noise constant, and Moving average (MA) component, which is the sum of past white noise errors with the expected value of the time-series.…”

Section: A Arma Modelsmentioning

confidence: 99%

Forecasting Network Traffic: A Survey and Tutorial With Open-Source Comparative Evaluation

et al. 2023

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This paper presents a review of the literature on network traffic prediction, while also serving as a tutorial to the topic. We examine works based on autoregressive moving average models, like ARMA, ARIMA and SARIMA, as well as works based on Artifical Neural Networks approaches, such as RNN, LSTM, GRU, and CNN. In all cases, we provide a complete and self-contained presentation of the mathematical foundations of each technique, which allows the reader to get a full understanding of the operation of the different proposed methods. Further, we perform numerical experiments based on real data sets, which allows comparing the various approaches directly in terms of fitting quality and computational costs. We make our code publicly available, so that readers can readily access a wide range of forecasting tools, and possibly use them as benchmarks for more advanced solutions.

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Section: A Arma Modelsmentioning

confidence: 99%

Forecasting Network Traffic: A Survey and Tutorial With Open-Source Comparative Evaluation

et al. 2023

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“…We used ARMAX to benchmark our methods because it is a widely applied methodology for time series regression [10,11,12,13,14]. This method expands the ARMA model with (a linear combination of) exogenic inputs (X).…”

Section: Armaxmentioning

confidence: 99%

GEFCOM 2014—Probabilistic Electricity Price Forecasting

Barta¹,

Borbély²,

Nagy³

et al. 2015

Intelligent Decision Technologies

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Abstract. Energy price forecasting is a relevant yet hard task in the field of multi-step time series forecasting. In this paper we compare a wellknown and established method, ARMA with exogenous variables with a relatively new technique Gradient Boosting Regression. The method was tested on data from Global Energy Forecasting Competition 2014 with a year long rolling window forecast. The results from the experiment reveal that a multi-model approach is significantly better performing in terms of error metrics. Gradient Boosting can deal with seasonality and autocorrelation out-of-the box and achieve lower rate of normalized mean absolute error on real-world data.

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A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks

Chen

Law

Aibin

2021

Telecom

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Much research effort has been conducted to introduce intelligence into communication networks in order to enhance network performance. Communication networks, both wired and wireless, are ever-expanding as more devices are increasingly connected to the Internet. This survey introduces machine learning and the motivations behind it for creating cognitive networks. We then discuss machine learning and statistical techniques to predict future traffic and classify each into short-term or long-term applications. Furthermore, techniques are sub-categorized into their usability in Local or Wide Area Networks. This paper aims to consolidate and present an overview of existing techniques to stimulate further applications in real-world networks.

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Towards Forecasting Low Network Traffic for Software Patch Downloads: An ARMA Model Forecast Using CRONOS

Cited by 3 publications

References 4 publications

Forecasting Network Traffic: A Survey and Tutorial With Open-Source Comparative Evaluation

Forecasting Network Traffic: A Survey and Tutorial With Open-Source Comparative Evaluation

GEFCOM 2014—Probabilistic Electricity Price Forecasting

A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks

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