An automatic forecasting method is proposed concerning automation problem in the field of linear time series forecasting. The method is on the basis of econometric theory and overcomes the difficulty to mine and forecast automatically with econometric models. The proposed algorithm is divided into 4 stages, i.e. preprocessing, unit root testing and stationary processing, modeling, and ultimately forecasting. Future values and trends would be estimated and forecasted precisely through the 4 stages of the algorithm according to input data without manual intervention. Experimental comparisons were made between the proposed algorithm and the 2 data driven forecasting algorithms, i.e. moving average method and Holt exponential smoothing method. It was demonstrated with the experimental results that automatic forecasting is feasible utilizing the proposed algorithm and higher accuracy can be acquired than these 2 data driven-based methods.