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

• Autorzy: Babu C. N. , Reddy B. E.
• Języki publikacji: EN

Abstrakty

EN

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. This suitability of hybrid ARIMA-ANN models is studied for both one-step ahead and multistep ahead prediction cases. 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.

Słowa kluczowe

EN

ANN; ANN training; ARIMA; Box-Jenkins methodology; hybrid ARIMA-ANN model; Internet traffic forecasting

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2015
• Tom: nr 1
• Strony: 67--75
• Opis fizyczny: Bibliogr. 24 poz., rys., tab.

Twórcy

autor

Babu C. N.

• Department of Computer Science and Engineering, JNT University College of Engineering, Anantapuramu, India

autor

Reddy B. E.

• Department of Computer Science and Engineering, JNT University College of Engineering, Anantapuramu, India

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