Forecasting demand for special telephone services. A case study

Future demand for services or goods is usually forecasted by fitting ARIMA models and using the optimal rules based on the squared error criterion. When analyzing a large number of time series describing Special Services in the telephone business, the authors found that a model with independent increments with stable distributions was more suitable and led to better predictions. It also described forecast errors adequately. This paper discusses the model, compares it with a state space model which is currently used for the problem, and applies several data analytic procedures to assess how well the model fits the data. A few remarks on the use of estimated forecast error size conclude the paper.