Forecasting Internet Traffic by Using Seasonal GARCH Models
- Authors
- Kim, Sahm
- Issue Date
- Dec-2011
- Publisher
- KOREAN INST COMMUNICATIONS SCIENCES (K I C S)
- Keywords
- Akaike information criterion (AIC); Internet traffic; root mean square error (RMSE); seasonal autoregressive-generalized autoregressive conditional heteroscedasticity (AR-GARCH); seasonal autoregressive integrated moving average (ARIMA)
- Citation
- JOURNAL OF COMMUNICATIONS AND NETWORKS, v.13, no.6, pp 621 - 624
- Pages
- 4
- Journal Title
- JOURNAL OF COMMUNICATIONS AND NETWORKS
- Volume
- 13
- Number
- 6
- Start Page
- 621
- End Page
- 624
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/21094
- DOI
- 10.1109/JCN.2011.6157478
- ISSN
- 1229-2370
1976-5541
- Abstract
- With the rapid growth of internet traffic, accurate and reliable prediction of internet traffic has been a key issue in network management and planning. This paper proposes an autoregressive-generalized autoregressive conditional heteroscedasticity (AR-GARCH) error model for forecasting internet traffic and evaluates its performance by comparing it with seasonal autoregressive integrated moving average (ARIMA) models in terms of root mean square error (RMSE) criterion. The results indicated that the seasonal AR-GARCH models outperformed the seasonal ARIMA models in terms of forecasting accuracy with respect to the RMSE criterion.
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Collections - College of Business & Economics > Department of Applied Statistics > 1. Journal Articles
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