In this study, a triple-phase support vector machine based neural network ensemble model
is proposed for exchange rates forecasting. In the first phase, many different single neural …

In this study, a triple-stage support vector regression (SVR) based neural network ensemble
forecasting model is proposed for foreign exchange rates forecasting. In the first stage …

In this study, a multistage nonlinear radial basis function (RBF) neural network ensemble
forecasting model is proposed for foreign exchanger rates prediction. In the process of …

This study proposes a novel forecasting approach–an adaptive smoothing neural network
(ASNN)–to predict foreign exchange rates. In this new model, adaptive smoothing …

Some studies revealed that foreign exchange market is one of the most volatile markets.
Due to its high volatility, foreign exchange rates forecasting is regarded as a rather …

Forecasting exchange rates is an important financial problem that is receiving increasing
attention especially because of its difficulty and practical applications. This paper proposes a …

In this paper, Functional link artificial recurrent neural network (FLARNN) ensemble
forecasting model is proposed for foreign exchange rates prediction. In the survey of existing …

Exchange rate forecasting is one of the most common subjects among the forecasting
problem field. Researchers and academicians from many different disciplines proposed …

This paper applies an integrated artificial neural network approach to forecast foreign
exchange rates between the US dollar and Chinese Renminbi. In order to obtain a better …

This research constructs a new forecasting model. Particle Swarm Optimization (PSO) is
utilized to select the optimal input layer neurons, and then predict exchange rates by the …