Automatic Forecasting of Double Seasonal Time Series with Applications on Mobility Network Traffic Prediction

• Activity Number: 84
• Type: Contributed
• Date/Time: Sunday, July 31, 2011 : 4:00 PM to 5:50 PM
• Sponsor: Business and Economic Statistics Section
• Author(s): Shu-Ngai Yeung*+ and Guang-Qin Ma and Tom S. Au
• Companies: AT&T Labs Research and AT&T Labs Research and AT&T Labs Research
• Keywords: Business Forecasting ; Combined Forecasts ; ARIMA ; Exponential Smoothing

Abstract

Automatic forecasting procedures are common in business practice where large number of time series are needed for forecast. One of such applications is on mobility network resource planning which requires accurate prediction of future peak usage at each cell tower location within the network. In this paper, we developed an automatic procedure based on univariate double seasonal ARIMA models (DSARIMA) to forecast time series database with multiple seasonal patterns. A large scale empirical study comparing automatic DSARIMA with double seasonal Exponential Smoothing (DSEXP) is performed using real mobile phone network data. We also considered the performance of combined forecasts of the two models based on OLS and variations. The results show that automatic DSARIMA models and combined forecast outperform DSEXP, especially in the forecasting horizon beyond one day ahead.