Future air traffic management (ATM) decision support systems (DSS) rely upon trajectory forecasting to adequately deliver benefits. These forecasts are subject to errors from a variety of sources. As the number and sophistication of ATM DSS capabilities grow, the interoperability between DSS will become more sensitive to the magnitude and variation in trajectory forecasting errors across DSS. T...

In this research, a hybrid wavelet-artificial neural network (WANN) and a geostatistical method were proposed for spatiotemporal prediction of the groundwater level (GWL) for one month ahead. For this purpose, monthly observed time series of GWL were collected from September 2005 to April 2014 in 10 piezometers around Mashhad City in the Northeast of Iran. In temporal forecasting, an artificial...

Forecasting strategies that are robust to structural breaks have earned renewed attention in the literature. They are built on weighted averages downweighting past information and include forecasting with rolling window, exponential smoothing or exponentially weighted moving average and forecast pooling. These simple strategies are particularly attractive because they are easy to implement, pos...

1 Singh, S. "Forecasting using a Fuzzy Nearest Neighbour Method", Proc. 6th International Conference on Fuzzy Theory and Technology , Fourth Joint Conference on Information Sciences (JCIS'98), North Carolina, vol. 1, pp.80-83, 1998 (23-28 October ,1998) ABSTRACT This paper explores a nearest neighbour pattern recognition method for time-series forecasting. A nearest neighbour method (FNNM) base...

This study investigates the performance of radial basis function networks (RBFN) in forecasting the monthly CO2 emissions of an electric power utility. We also propose a method for input variable selection. This method is based on identifying the general relationships between groups of input candidates and the output. The effect that each input has on the forecasting error is examined by removi...

Original scientific paper As the stock market volatility is highly nonlinear, coupling and time varying, it is difficult to predict by the traditional forecasting methods. For explaining the existing problems of the current volatility forecasting method, we use the model based on the weighted least squares support vector regression (WLSSVR) method to predict the stock index volatility in this p...

To effectively predict auto sales and improve the competitiveness of automotive enterprise, the characteristics of actual auto sales were analyzed, owing to the seasonal fluctuations and the nonlinearity of monthly sales, the combination forecasting model based on seasonal Index and RBF neural network was proposed. The weights of the two single models were computed using mean absolute percentag...

In the paper a relatively simple yet powerful and versatile technique for forecasting time series data – simple exponential smoothing is described. The simple exponential smoothing (SES) is a short-range forecasting method that assumes a reasonably stable mean in the data with no trend (consistent growth or decline). It is one of the most popular forecasting methods that uses weighted moving av...

Diversity and accuracy are the two key factors that decide the ensemble generalization error. Constructing a good ensemble method by balancing these two factors is difficult, because increasing diversity is at the cost of reducing accuracy normally. In order to improve the performance of an ensemble while avoiding the difficulty derived of balancing diversity and accuracy, we propose a novel me...

Forecasting industrial production is essential for efficient planning by managers. Although there are many statistical and mathematical methods for prediction, the use of intelligent algorithms with desirable features has made significant progress in recent years. The current study compared the accuracy of the Artificial Neural Networks (ANN) and Adaptive Nero-Fuzzy Inference System (ANFIS) app...