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...

In this paper one-step-ahead and multiple-step-ahead predictions of time series in disturbed open loop and closed loop systems using Gaussian process models and TS-fuzzy models are described. Gaussian process models are based on the Bayesian framework where the conditional distribution of output measurements is used for the prediction of the system outputs. For one-step-ahead prediction a local...

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...

Multistep-ahead prediction is the task of predicting a sequence of values in a time series. A typical approach, known as multi-stage prediction, is to apply a predictive model step-by-step and use the predicted value of the current time step to determine its value in the next time step. This paper examines two alternative approaches known as independent value prediction and parameter prediction...

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...

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; : : :...

In this paper, the normalized prediction error of the electroencephalogram (EEG) signal recorded at five different mental tasks was computed. The results indicate that there exists predictability in the EEG signal beyond the baseline prediction of the mean and the one-stepahead normalized prediction error of EEG signal vary greatly when different mental tasks are implemented, which implies that...

This literature focuses on grid resource monitoring and prediction, representative monitoring and prediction systems are analyzed and evaluated, then monitoring and prediction strategies for grid resources are summarized and discussed, recommendations are also given for building monitoring sensors and prediction models. During problem definition, one-step-ahead prediction is extended to multi-s...