Neuro-PCA-Factor Analysis in Prediction of Time Series Data

Many related parameters have been considered to predict any physical problem in the world. Many of them are not significant or they are highly correlated with other parameters. But, some parameters are playing significant role in prediction of the problem. These are giving necessary and sufficient information and not correlated with the others. The output of the problem can be predicted by considering fewer significant parameters instead of all. In this paper, an effort has been made to find the significant environmental parameters in production of mustard plant using principal component and factor analysis. The environmental parameters like maximum and minimum temperature, rain fall, maximum and minimum humidity, soil moisture at different depth and sun shine have been affected the growth of mustard plant. The affect has made by all parameters are not same and more complex to predict the growth of muster plant with all parameters. The principal component and factor analysis have been used here to reduce the environmental parameters. These analyses have been used to find the significant parameters that have been greatly participated in growth of mustard plant. Finally, artificial neural network has been applied on highly significant parameters to predict the production of mustard plant at maturity.