A Weighted Moving Average Process for Forecasting Shou Hsing Shih Chris P. Tsokos Department of Mathematics and Statistics University of South Florida, USA Abstract The object of the present study is to propose a forecasting model for a nonstationary stochastic realization. The subject model is based on modifying a given time series into a new k- time moving average time series to begin the development of the model. The study is based on the autoregressive integrated moving average process along with its analytical constrains. The analytical procedure of the proposed model is given. A stock XYZ selected from the Fortune 500 list of companies and its daily closing price constitute the time series. Both the classical and proposed forecasting models were developed and a comparison of the accuracy of their responses is given. Keywords: ARIMA; Moving Average; Stock; Time Series Analysis Introduction Time series analysis and modeling plays a very important role in forecasting, especially when our initial stochastic realization is nonstationary in nature. Some of the interesting and useful publications related to the subject area are Akaike (1974), Banerjee et al. (1993), Box et al. (1994), Brockwell and Davis (1996), Dickey and Fuller (1979), Dickey et al. (1984), Durbin and Koopman (2001), Gardner et al. (1980), Harvey (1993), Jones (1980), Kwiatkowski et al. (1992), 1