A forecasting model for a nonstationary stochastic realization is proposed based on modifying a given time series into a new k-time moving average time series. 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.
Shih, Shou Hsing and Tsokos, Chris P.
"A Weighted Moving Average Process for Forcasting,"
Journal of Modern Applied Statistical Methods:
2, Article 26.
Available at: http://digitalcommons.wayne.edu/jmasm/vol6/iss2/26