A Relative Study Of Arima With Cdrann For Finding Best Future Predictor

Abstract

Prediction analysis is used for gaining the optimal value in real world problems. Artificial Neural Network with deep learning forms a deep neural network. It will help to create a novel future prediction for real world problem. For time-series problem like stock market prediction, there is a need for best predictor for future price. To achieve this, a relative analysis is must. This research work helps to identify the best future predictor for stock by relating the proposed model CDRANN with the standard ARIMA model. The performance result shows that for time series dataset CDRANN model was able to perform better than the ARIMA method.