forecast nonlinear ARMA model based simulated data and real data of financial returns. The forecasting ability of the recurrent …, recurrent artificial neural network based ARMA model and feed-forward SVR based ARMA model) by using two forecasting accuracy … evaluation metrics (NSME and sign) and robust Diebold–Mariano test. The results reveal that for one-step-ahead forecasting, the …

-output forecasting. This approach allows modelling the cross-dependencies between a given set of input variables and generating a …, the performance of the proposed model is assessed in a multiple-step-ahead forecasting comparison. The results of the … experiment in a multivariate setting show that the Gaussian process regression model significantly improves the forecasting …

purpose is mentioned as well. Forecasting with complex dynamic systems, albeit less frequently applied to economic forecasting …

Neural Network (GRNN) and compare its performance with a variety of forecasting techniques, including Multi … model is used for benchmark comparison. Our findings show that GRNN not only has a higher degree of forecasting accuracy but …

explain risk premium. Investigating whether these factors are useful in forecasting stock returns remains active research in …

efficient alternative tool for forecasting. The MATLAB algorithm we propose is provided in appendix for further applications …

The topic of this chapter is forecasting with nonlinear models. First, a number of well-known nonlinear models are … linear model. There exist relatively large studies in which the forecasting performance of nonlinear models is compared with …