Título: Comparative study of Financial Time Series Prediction By Artificial Neural Network with Gradient Descent Learning
Autores: ARKA ghosh; Seacom Engineering College
Fecha: 2012-04-14
Publicador: BRAIN: Broad Research in Artificial Intelligence and Neuroscience
Fuente:
Tipo: Peer-reviewed Article
Tema: Artificial Intelligence;Neural Information Processing
Financial Forecasting,Financial Timeseries Feedforward Multilayer Artificial Neural Network,Recurrent Timedelay Neural Network,Backpropagation,Gradient descent.

Descripción: Financial forecasting is an example of a signal processing problem which is challenging due to Small 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.
Idioma: Inglés

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