The object of the present study is to introduce three analytical time series models for the purpose of developing more effective economic and environmental forecasting models, among others. Given a stochastic realization, stationary or nonstationary in nature, one can utilize exciting methodology to develop an autoregressive, moving average or a combination of both for short and long term forecasting. In the present study we analytically modify the stochastic realization utilizing (a) a k-th moving average, (b) a k-th weighted moving average and (c) a k-th exponential weighted moving average processes. Thus, we proceed in developing the appropriate forecasting models with the new (modified) time series using the more recent methodologies in the subject matter. Once the proposed statistical forecasting models have been developed, we proceed to modify the analytical process back into the original stochastic realization. The proposed methods have been successfully applied to real stock data from a Fortune 500 company. A similar forecasting model was developed and evaluated for the daily closing price of S&P Price Index of the New York Stock Exchange. The proposed forecasting model was developed along with the statistical model using classical and most recent methods. The effectiveness of the two models was compared using various statistical criteria. The proposed models gave better results. Atmospheric temperature and carbon dioxide, CO2, are the two variables most attributable to GLOBAL WARMING. Using the proposed methods we have developed forecasting statistical models for the continental United States, for both the atmospheric temperature and carbon dioxide. We have developed forecasting models that performed much better than the models using the classical Box-Jenkins type of methodology. Finally, we developed an effective statistical model that relates CO2 and temperature; that is, knowing the atmospheric temperature we can at the specific location estimate the carbon dioxide and vice versa.