An Improved OIF Elman Neural Network Model with Direction Profit Factor and Its Applications

Li, Ming; Wang, Limin; Liu, Yang; Liu, Ying; Sun, Qian; Han, Xuming

doi:10.1109/icmv.2009.39

Cited by 2 publications

(1 citation statement)

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“…Liang Qibin radial (RBF) neural networks and Elman neural network to predict effect of decontamination compound substrates in constructed wetland, And verify Elman neural network forecasting method of high accuracy [8] . OIF Elman neural network is proposed by Li Ming to predict the stock market's composite index, results show that the model for stock market prediction is feasible and effective, is very good at predicting potentials and broad application prospects [9] . However it is difficult to select neural network input variable because of the Stock price is influenced by many factors.…”

Section: Introductionmentioning

confidence: 99%

Application on stock price prediction of Elman neural networks based on principal component analysis method

Shi

Liu

2014

2014 11th International Computer Conference on Wavelet Actiev Media Technology and Information Processing(ICCWAMTIP)

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Study on the prediction of stock price has great theoretical significance and application value. Traditional stock forecasting methods cannot fit and analysis highly nonlinear, multi-factors of stock market well, there are problems such as the prediction accuracy is not high, the slow training speed etc. In order to improve the accuracy of stock price forecasting, this paper proposes a prediction method of Elman neural network model based on principal component analysis method. In order to better compare results, establish structure same BP network and Elman network, forecast for stock data; then using principal component analysis filter factors of significant effect on stock prices, Elman neural network model based on principal component analysis method, and compared with single Elman network and BP networks prediction results. Result shows BP network convergence is relatively slow, train for a long time, and could converge to a local minimum; Elman network training time is short, the error bars for smoother and more stable performance; Elman neural network model based on principal component analysis with higher accuracy, faster network speeds.

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Section: Introductionmentioning

confidence: 99%