A hybrid evolutionary dynamic neural network for stock market trend analysis and prediction using unscented Kalman filter

Dynamic neural network (DNN) models provide an excellent means for forecasting and prediction of nonstationary time series. A neural network architecture, known as locally recurrent neural network ((LRNN) [71], is preferred to the traditional multilayer perceptron (MLP) because the time varying nature of a stock time series can be better represented using LRNN. The use of LRNN has demonstrated superiority in comparison to the widely used neural networks like the multilayered perceptron (MLP) network, radial basis function (RBF) neural network, and wavelet neural networks (WNN), etc. in predicting highly fluctuating time series databases like electrical load, electricity price, and financial markets.