Forecasting internet traffic by using seasonal GARCH models

Kim, Sahm

doi:10.1109/jcn.2011.6157478

Cited by 29 publications

(10 citation statements)

References 9 publications

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“…The FIS prediction methodology is firstly evaluated on the synthetically generated data using Mackey-glass delay differential equation given by (6) and shown in Fig. 3.…”

Section: A Dataset Descriptionmentioning

confidence: 99%

“…It is found that traffic exhibited non-stationary and non-linear properties and hence, threshold autoregressive (TAR) model [5] has also been considered. More recently, seasonal ARIMA and seasonal generalized autoregressive conditional heteroscedasticity (seasonal GARCH) models have been used to forecast periodically non-stationary traffic and for dynamic bandwidth provisioning [6].…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy inference system for Internet traffic load forecasting

Maurya

Minz

2012

2012 National Conference on Computing and Communication Systems

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Conventional statistical analysis of Internet traffic data is often employed to determine traffic distribution, to summarize users behavior patterns, or to predict future network traffic for network management and planning. However, the statistical techniques like autoregressive integrated moving average (ARIMA) models fail to capture some peculiar network traffic characteristics like Self-similarity, Long-range dependency (LRD), etc. With rapid growth of Internet, accurate and reliable forecasting is essential for the network resource management. Therefore, the present work explores to utilize computational intelligence techniques like fuzzy logic to predict Internet traffic data pattern. This paper presents the development of Fuzzy inference system (FIS) for one benchmark dataset and one real dataset. The experimental results of the FIS of the benchmark dataset Mackey-Glass series and real dataset JNU LAN traffic data collected for one hour are presented. The performance of the FIS is estimated using cross-validation method by measuring root mean square error (RMSE).

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“…The FIS prediction methodology is firstly evaluated on the synthetically generated data using Mackey-glass delay differential equation given by (6) and shown in Fig. 3.…”

Section: A Dataset Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzzy inference system for Internet traffic load forecasting

Maurya

Minz

2012

2012 National Conference on Computing and Communication Systems

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“…In addition, ARIMA/GARCH was proposed to predict internet traffic and showed better results compared with Minimum Mean Square Average (MMSE) and Fractional ARIMA (FARIMA). After that, based on RMSE criterion, Kim et al [6] indicated that seasonal AR-GARCH outperformed seasonal ARIMA in predicting internet traffic. In terms of modeling and predicting mobile communication traffic, seasonal ARIMA models were presented by Shu et al in [7], Guo et al in [8] and Miao et al in [9].…”

Section: Introductionmentioning

confidence: 99%

A Novel Procedure to Model and Forecast Mobile Communication Traffic by ARIMA/GARCH Combination Models

Tran¹,

Li²,

Trinh³

2016

Proceedings of 2016 International Conference on Modeling, Simulation and Optimization Technologies and Applications (MSOTA2016)

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Abstract-Mobile traffic modeling and forecasting are the key techniques in terms of network optimization and management because better network management can be achieved through improving the forecasting accuracy. While mobile traffic has been studied extensively and proved to be effectively modeled with ARIMA models, the volatility effect in mobile traffic series that results in forecasting errors was seldom mentioned. In this study, a multiplicative seasonal ARIMA/GARCH building procedure is proposed to show that volatility effect appearing in mobile traffic series can be processed by GARCH models. Our proposed procedure combines several evaluating parameters such as Akaike Information Criterion (AIC), Schwarz Criterion (SIC), forecast performance evaluation information and residual correlogram to find out the most suitable model, based on which descriptive statistics are used to get the final choice. This work indicates that the mobile traffic series can be better modeled and forecasted by applying GARCH models based on a multiplicative seasonal ARIMA.

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“…With the implicit assumption of homoskedasticity, GARCH is absolutely efficient in investigating the volatility characteristics of time series. Therefore, the combination of ARIMA and GARCH is a good choice to give a better result in capturing and forecasting time series such as wireless traffic data [5]- [7], crude oil prices data [8], inflation data [9], or internet traffic [10].…”

Section: Introductionmentioning

confidence: 99%

A Multiplicative Seasonal ARIMA/GARCH Model in EVN Traffic Prediction

Beijing¹,

Hanoi²,

Vietnam³

2015

IJCNS

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This paper highlights the statistical procedure used in developing models that have the ability of capturing and forecasting the traffic of mobile communication network operating in Vietnam. To build such models, we follow Box-Jenkins method to construct a multiplicative seasonal ARIMA model to represent the mean component using the past values of traffic, then incorporate a GARCH model to represent its volatility. The traffic is collected from EVN Telecom mobile communication network. Diagnostic tests and examination of forecast accuracy measures indicate that the multiplicative seasonal ARIMA/GARCH model, i.e. ARIMA (1, 0, 1) × (0, 1, 1)24/GARCH (1, 1) shows a good estimation when dealing with volatility clustering in the data series. This model can be considered to be a flexible model to capture well the characteristics of EVN traffic series and give reasonable forecasting results. Moreover, in such situations that the volatility is not necessary to be taken into account, i.e. short-term prediction, the multiplicative seasonal ARIMA/GARCH model still acts well with the GARCH parameters adjusted to GARCH (0, 0).

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Forecasting internet traffic by using seasonal GARCH models

Cited by 29 publications

References 9 publications

Fuzzy inference system for Internet traffic load forecasting

Fuzzy inference system for Internet traffic load forecasting

A Novel Procedure to Model and Forecast Mobile Communication Traffic by ARIMA/GARCH Combination Models

A Multiplicative Seasonal ARIMA/GARCH Model in EVN Traffic Prediction

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