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Financial forecasting is an example of a signal processing problem which ischallenging due to Small sample sizes, high noise, non-stationarity, andnon-linearity,but fast forecasting of stock market price is very important forstrategic business planning.Present study is aimed to develop a comparativepredictive model with Feedforward Multilayer Artificial Neural Network &Recurrent Time Delay Neural Network for the Financial TimeseriesPrediction.This study is developed with the help of historical stockpricedataset made available by GoogleFinance.To develop this prediction modelBackpropagation method with Gradient Descent learning has beenimplemented.Finally the Neural Net, learned with said algorithm is found to beskillful predictor for non-stationary noisy Financial Timeseries.

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