In this thesis, application of GMDH Algorithm to real life problems is studied. A particular type of GMDH Algorithm namely TMNN is chosen for this purpose. An effort is made to forecast S&P Index Closing Value with the help of the forecaster.

The goal of time series forecasting is to identify the underlying pattern and use these patterns to predict the future path of the series. To capture the future path of a dynamic stock market variable is one of the toughest challenges.