Hybrid model is a popular forecasting model in renewable energy related forecasting applications. Wind speed forecasting, as a common application, requires fast and accurate forecasting models. This paper introduces an Empirical Mode Decomposition (EMD) followed by a k Nearest Neighbor (kNN) hybrid model for wind speed forecasting. Two configurations of EMD-kNN are discussed in details: an EMD-...

Accumulation of soluble salts in arid areas which are similar to most regions of Iran is inevitable in soil surface and profile because of low precipitation and high evaporation. High concentration of soluble salts in soil profile caused severe problems for root water uptake thus plant growth stopped. Reducing soil salinity to optimized content by leaching and avoiding soil pounding must be con...

artificial neural networks (anns) are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy. however, despite of all advantages cited for artificial neural networks, they have data limitation and need to the large amount of historical data in order to yield accurate results. therefore, the...

In this research a three staged hybrid genetic-fuzzy systems modeling methodology is developed and applied to an empirical data set in order to determine the hidden fuzzy if-then rules. The empirical data was collected in an earlier study in order to establish the relations among human capital, organizational support and innovativeness. The results demonstrate that the model based on the fuzzy ...

 In this paper we consider a general setting of skew-symmetric distribution which was constructed by Azzalini (1985), and its properties are presented. A suitable empirical estimator for a skew-symmetric distribution is proposed. In data analysis, by comparing this empirical model with the estimated skew-normal distribution, we show that the proposed empirical model has a better fit in den...

in this study, a hybrid intelligent model has been designed to predict groundwater inflow to a mine pit during its advance. novel hybrid method coupling artificial neural network (ann) with genetic algorithm (ga) called ann-ga, was utilised. ratios of pit depth to aquifer thickness, pit bottom radius to its top radius, inverse of pit advance time and the hydraulic head (hh) in the observation w...

This study proposes a hybrid model, which combines GARCH and Neural Network, for estimating VAR in Nasdaq 100 and Dow Jones futures index market. Empirical results demonstrated that the hybrid method has certain outperformed the conventional method (historical simulation, variance/covariance and the Monte Carlo simulation) in estimating VAR. In terms of accuracy, the hybrid method is superior t...

ANNARIMA that combines both autoregressive integrated moving average (ARIMA) model and artificial neural network (ANN) model is a valuable tool for modeling and forecasting nonlinear time series, yet the over-fitting problem is more likely to occur in neural network models. This paper provides a hybrid methodology that combines both radial basis function (RBF) neural network and auto regression...

according to recent developments of predicting methodsin financial markets, and since the stock price is one of the mostimportant factors for investment decision-making, and its predictioncan play an important role in this field, the aim of this study is toprovide a model to predict the stock price movement with highaccuracy. accordingly, a hybrid model for predicting the stock pricemovement us...

traditional dea models deal with measurements of relative eciency ofdmus regarding multiple - inputs vs. multiple-outputs. one of the drawbacksof these model is the neglect of intermediate products or linkong activities. af-ter pointing out needs for inclusion of them to dea models. we propose hybridmodel that can deal with intermediate products formally using this model wecan evaluate divisio...