Objective: In general, financial time series such as stock indexes have nonlinear, mutable and noisy behavior. Structural and statistical models and machine learning-based models are often unable to accurately predict series with such a behavior. Accordingly, the aim of the present study is to present a new hybrid model using the advantages of the GMDH method and Non-dominated Sorting Genetic A...

in order to determine hydrological behavior and water management of sepidroud river (north of iran-guilan) the present study has focused on stream flow prediction by using artificial neural network. ten years observed inflow data (2000-2009) of sepidroud river were selected; then these data have been forecasted by using neural network. finally, predicted results are compared to the observed dat...

INTRODUCTION Selection of an appropriately-powered IOL is a complex issue, especially in eyes with an axial length of less than 20 mm in pediatric cataract. OBJECTIVE To assess the accuracy of IOL power calculation formulae in pediatric cataracts in eyes with an axial length of less than 20 mm. MATERIALS AND METHODS The records of children less than 15 years old with congenital cataract who...

Robust estimators of the prediction error of a linear model are proposed. The estimators are based on the resampling techniques cross-validation and bootstrap. The robustness of the prediction error estimators is obtained by robustly estimating the regression parameters of the linear model and by trimming the largest prediction errors. To avoid the recalculation of timeconsuming robust regressi...

Typically the true error of ANN prediction model is estimated by testing the trained network on new data not used in model construction. Four well-studied statistical error estimation methods: cross-validation, group cross-validation, jackknife and bootstrap are reviewed and are presented as competing error estimation methodologies that could be used to evaluate and validate ANN prediction mode...