Title :
A time series model of long-term NSFNET backbone traffic
Author :
Groschwitz, Nancy K. ; Polyzos, George C.
Author_Institution :
Dept. of Comput. Sci. & Eng., California Univ., San Diego, La Jolla, CA, USA
Abstract :
We used time series analysis to create detailed forecasts of future NSFNET backbone traffic. The resulting autoregressive integrated moving average process (ARIMA) model made quite accurate forecasts of traffic levels up to a year in advance. It appears that the model can make reasonable predictions for two or more years into the future, suggesting that ARIMA modeling has great promise as a tool for long-range NSFNET forecasting and planning
Keywords :
Internet; autoregressive moving average processes; telecommunication traffic; time series; ARIMA model; Internet; NSFNET backbone traffic; autoregressive integrated moving average; long-range forecasting; long-range planning; time series analysis; time series model; traffic forecasting; Autoregressive processes; Electric shock; Parameter estimation; Pattern analysis; Predictive models; Spine; Stochastic processes; Time series analysis; Traffic control; White noise;
Conference_Titel :
Communications, 1994. ICC '94, SUPERCOMM/ICC '94, Conference Record, 'Serving Humanity Through Communications.' IEEE International Conference on
Conference_Location :
New Orleans, LA
Print_ISBN :
0-7803-1825-0
DOI :
10.1109/ICC.1994.368876
