We propose a hybrid method for forecasting the wind speed. The wind speed data is first decomposed into intrinsic mode functions (IMFs) with empirical mode decomposition. Based on the partial autocorrelation factor of the individual IMFs, adaptive methods are then employed for the prediction of IMFs. Least squares-support vector machines are employed for IMFs with weak correlation factor, and a...

A number of research had been carried out to forecast stock price based on technical indicators, which rely purely on historical stock price data. Nevertheless, their performance is not always satisfactory. In this paper, the effect of using hybrid market indicators of technical, fundamental indicators and experts opinion for stock price prediction is examined. Input variables extracted from th...

In this paper we explore the influence of inflation rigidity on the monetary policy transmission mechanism in a model featuring the hybrid Phillips curve. We compare the New Keynesian Phillips curve and the hybrid Phillips curve for their contribution to reproducing stylized empirical facts about business cycles driven by monetary policy shocks. Variables’ induced volatility and the dynamics of...

drought is random and nonlinear phenomenon and using linear stochastic models, nonlinear artificial neural network and hybrid models is advantaged for drought forecasting. this paper presents the performances of autoregressive integrated moving average (arima), direct multi-step neural network (dmsnn), recursive multi-step neural network (rmsnn), hybrid stochastic neural network of directive ap...

Accurate and reliable streamflow forecasting plays an important role in various aspects of water resources management such as reservoir scheduling and water supply. This paper shows the development of a novel hybrid model for streamflow forecasting and demonstrates its efficiency. In the proposed hybrid model for streamflow forecasting, the empirical wavelet transform (EWT) is firstly employed ...

this paper shows the role of the general dynamic model in empirical research of production technology in agriculture. the model is a first order autoregressive multivariate specification, first developed by anderson and blundell. this model is general enough to nest several simpler dynamic as well as static models within it. therefore, it provides a framework for applying classical testing ...

Theoretical spatial models of electoral voting tend to predict either convergence to an electoral mean (when voting is probabilistic) or chaos (when voting is deterministic). Here, we construct an empirical model of voting for the Israeli Knesset in 1992 (based on a large electoral sample and on analysis of party declarations). The probabilistic voting model so estimated fits the known election...