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

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

2011-11-21

arxiv.org/abs/1111.4930v2

International Journal Of Scientific & Engineering Research ISSN-2229-5518 Volume 3 Issue 1 January2012

Computer Science

Neural and Evolutionary Computing

Scientific paper

Financial forecasting is an example of a signal processing problem which is challenging due to Small sample sizes, high noise, non-stationarity, 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 with Feedforward Multilayer Artificial Neural Network & Recurrent Time Delay Neural Network 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 with Gradient Descent learning has been implemented.Finally the Neural Net, learned with said algorithm is found to be skillful predictor for non-stationary noisy Financial Timeseries.

Affiliated with

Also associated with

No associations

Rating

Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-377743