Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning

Financial forecasting is an example of a signal processing problem w hich is challenging due to Small sizes, high noise, nonstationarity, and non-linearity,but fast forecasting of stock market price is very important for strategic business planning.Present study is aimed to develop a comparative predictive model w ith Feedforward Multilayer Artif icial Neural Netw ork & Recurrent Time Delay Neural Netw ork for the Financial Timeseries Prediction.This study is developed with the help of historical stockprice dataset made available by GoogleFinance.To develop this prediction model Backpropagation method w ith Gradient Descent learning has been implemented.Finally the Neural Net ,learned w ith said algorithm is found to be skillful predictor for non-stationary noisy Financial Timeseries. Key Words—. Financial Forecasting,Financial Timeseries Feedforward Multilayer Artif icial Neural Netw ork,Recurrent Timedelay Neural Netw ork,Backpropagation,Gradient descent. ——————————  ——————————