Performance Comparison of Four New ARIMA-ANN Prediction Models on Internet Traffic Data

Prediction of Internet traffic time series data (TSD) is a challenging research problem, owing to the complicated nature of TSD. In literature, many hybrids of auto-regressive integrated moving average (ARIMA) and artificial neural networks (ANN) models are devised for the TSD prediction. These hybr...

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Bibliographic Details
Published in:Journal of Telecommunications and Information Technology 2015 (1), p.67-75
Main Authors: Babu, C Narendra, Reddy, B Eswara
Format: Article
Language:English
Subjects:
ANN
ANN training
ARIMA
Box-Jenkins methodology
Error analysis
Errors
hybrid ARIMA-ANN model
Internet
Internet traffic forecasting
Learning theory
Mathematical models
Neural networks
Traffic engineering
Traffic flow
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Summary:Prediction of Internet traffic time series data (TSD) is a challenging research problem, owing to the complicated nature of TSD. In literature, many hybrids of auto-regressive integrated moving average (ARIMA) and artificial neural networks (ANN) models are devised for the TSD prediction. These hybrid models consider such TSD as a combination of linear and non-linear components, apply combination of ARIMA and ANN in some manner, to obtain the predictions. Out of the many available hybrid ARIMA-ANN models, this paper investigates as to which of them suits better for Internet traffic data. For the purpose of the study, Internet traffic data is sampled at every 30 and 60 minutes. Model performances are evaluated using the mean absolute error and mean square error measurement. For one-step ahead prediction, with a forecast horizon of 10 points and for three-step prediction, with a forecast horizon of 12 points, the moving average filter based hybrid ARIMA-ANN model gave better forecast accuracy than the other compared models.
ISSN:1509-4553
1899-8852
DOI:10.26636/jtit.2015.1.780