modelling and forecasting stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. this nonlinearity affects the efficiency of the price characteristics. using an artificial neural network (ann) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...

Modelling and forecasting Stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. This nonlinearity affects the efficiency of the price characteristics. Using an Artificial Neural Network (ANN) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...

The traditional multi-step ahead prediction is based on sequential algorithm to run multi-step ahead prediction and it brings error propagation problem. Furthermore, the prediction error of multi-step ahead includes both system and propagation errors. Therefore, how to decrease the propagation error has become an important issue in multi-step ahead prediction. In this study we had used the para...

We review existing approaches in using neural networks for solving multi-step-ahead prediction problems. A few experiments allow us to further explore the relationship between the ability to learn longer-range dependencies and performance in multi-stepahead prediction. We eventually focus on characteristics of various multi-step-ahead prediction problems that encourage us to prefer one method o...

We consider the problem of multi-step ahead prediction in time series analysis using the non-parametric Gaussian process model. -step ahead forecasting of a discrete-time non-linear dynamic system can be performed by doing repeated one-step ahead predictions. For a state-space model of the form , the prediction of at time is based on the point estimates of the previous outputs. In this paper, w...

The drawback of indirect multi-step-ahead prediction is error accumulation. In order to tackle this problem and improve the capacity of adaptive time delay neural network (ATNN) for prediction, a three-stage prediction model SATNN based on spline interpolation and ATNN is presented. With spline interpolation and ATNN, the impact of last prediction errors that would be iterated into the model fo...

We consider the problem of multi-step ahead prediction in time series analysis using the non-parametric Gaussian process model. k-step ahead forecasting of a discrete-time non-linear dynamic system can be performed by doing repeated one-step ahead predictions. For a state-space model of the form yt = f(yt 1; : : : ; yt L), the prediction of y at time t+ k is based on the estimates ŷt+k 1; : : :...