A New Method for Short Time Series Forecasting

We propose a procedure to forecast short time series with stable seasonal pattern. This new method is motivated by the observations that short time series arise in many situations for the fierce competition. The quantity to be predicted is a yearly accumulation assuming that the partially accumulated data within the year are available. A simple model is proposed to describe the relationship between the yearly accumulation and partial accumulation and analytic results are obtained for both the point prediction and the predicative distribution. A comparison will be conducted between this model and traditional time series forecasting model with data from telecommunication industry. This method works better than the traditional models when only small amount of data are available. It can also be applied to forecast individual observations with a proper disaggregation algorithm