Most exchange rates are volatile and mainly rely on the principle of supply and demand. Millions of people around the world are influenced, one way or another, by the variation in exchange rates. In this research we demonstrate that the Artificial Intelligence, specifically Artificial Neural Networks (ANN), can improve the accuracy of forecasting exchange rates compared to statistical technique...

Developing reliable estimates of streamflow prediction are crucial for water resources management and flood forecasting purposes. The objectives of this study are to investigate the potential of support vector machines (SVM) model for streamflow forecasting at ungaged sites, and to compare its performance with other statistical method of multiple linear regression (MLR). Three quantitative stan...

Accurate forecasting of rainfall has been one of the most important issues in hydrological research. In this paper, a novel neural network technique, support vector regression (SVR), to monthly rainfall forecasting. The aim of this study is to examine the feasibility of SVR in monthly rainfall forecasting by comparing it with back–propagation neural networks (BPNN) and the autoregressive integr...

This paper explores a nearest neighbour pattern recognition method for time-series forecasting. A nearest neighbour method (FNNM) based on fuzzy membership values is developed. The main aim of the forecasting algorithm is to make single point forecasts into the future on the basis of past nearest neighbours. The nearest neighbours are selected using a membership threshold value. The results inc...

People evidence significant inaccuracies when predicting their response to many emotional life events. One unanswered question is whether such affective forecasting errors are due to participants’ poor estimation of their initial emotional reactions (an initial intensity bias), poor estimation of the rate at which these emotional reactions diminish over time (a decay bias), or both. The present...

Recent evidence suggests that many economic time series are subject to structural breaks, yet little is known about the properties of alternative forecasting methods for such data. This paper proposes a new method for determining the window size that explores the trade-off between bias and forecast error variance to minimize the mean squared forecast error in the presence of breaks in autoregre...

In this study we develop the hybrid models for forecasting in agricultural production planning. Real data of Thailand’s orchid export and Thailand’s pork product are used to validate candidate models. Autoregressive Integrate Moving Average (ARIMA) is also selected as a benchmarking to compare other developed models. The main concept of building the models is to combine different forecasting te...

This paper presents a new approach for short-term load forecasting (STLF). Curve fitting prediction and time series models are used for hourly loads forecasting of the week days. The curve fitting prediction (CFP) technique combined with genetic algorithms (GAs) is used for obtaining the optimum parameters of Gaussian model to obtain a minimum error between actual and forecasted load. A new tec...

By analysis of historical data of the ionosphere, it is suggested to apply grey theory to ionospheric short-term forecasting, grey range information entropy is defined to determine the optimum grey length of the sample sequence, the prediction model based on residual error is constructed, and the observation data of multiple ionospheric observation stations in China are adopted for test. The pr...