This work is an attempt to examine empirically the best ARIMA model for forecasting. Average daily share price indices of the data series of Square Pharmaceuticals Limited (SPL) have been used for this purpose. At first the stationarity condition of the data series are observed by ACF and PACF plots, then checked using the Statistics such as Ljung-Box-Pierce Q-statistic and Dickey-Fuller test statistic. It has been found that the average daily share price indices of the data series of Square Pharmaceuticals Limited (SPL) are non-stationary. The average daily share price indices of SPL data series are non-stationary even after log-transformation. But after taking first difference of logarithmic values of SPL data series, the same types of plots and the same types of statistics show that the data is stationary. The best ARIMA model have been selected by using the criteria such as AIC, AICc, SIC, AME, RMSE and MAPE etc. To select the best ARIMA model the data split into two periods, viz. estimation period and validation period. The model for which the values of criteria are smallest is considered as the best model. Hence, ARIMA (2, 1, and 2) is found as the best model for forecasting the SPL data series. Then, forecasts of the data have been made using selected type of ARIMA model. Finally, the values of ADSPI of SPL up to February 2012 are predicted and reported in the study.