An artificial neural forecasting model is developed for air transport passenger analysis. It uses a preprocessing method that decomposes information to reveal relevant features from the data. It is found that neural processing outperforms the traditional econometric approach and offers...

Prediction of water flow rate in a photovoltaic water pumping system (PVWPS) is of high importance for investors who wish to achieve an efficient management of water demand in remote and desert areas. In this paper, different prediction methods based on Artificial Neural Networks (ANNs) have...

This paper presents a comparison of various forecasting approaches, using time series analysis, on mean hourly wind speed data. In addition to the traditional linear (ARMA) models and the commonly used feed forward and recurrent neural networks, other approaches are also examined including the...

This paper presents a novel method for the forecasting of mean hourly wind speed data using time series analysis. The initial point for this approach is mainly the fact that none of the forecasting approaches for hourly data, that can be found in the literature, based on time series analysis or...

Comparison of two techniques for wind speed forecasting in the South Coast of the state of Oaxaca, Mexico is presented in this paper. The Autoregressive Integrated Moving Average (ARIMA) and the Artificial Neural Networks (ANN) methods are applied to a time series conformed by 7 years of wind...

Prediction is very important in business planning. The ability to accurately predict the future is fundamental to many decision activities in sales, marketing, production, inventory control, personnel, and many other functional areas of business. Time series modeling approach is one of the major...

To improve ATMs’ cash demand forecasts, this paper advocates the prediction of cash demand for groups of ATMs with similar day-of-the week cash demand patterns. We first clustered ATM centers into ATM clusters having similar day-of-the week withdrawal patterns. To retrieve...

To obtain, over medium term periods, wind speed time series on a site, located in the southern part of the Paris region (France), where long recording are not available, but where nearby meteorological stations provide large series of data, use was made of ANN based models. The performance of...

A novel technique is presented based on self-organizing neural networks for prediction of fertilizer distribution patterns of spreaders as a function of spreader settings and fertilizer properties. The main aim of the presented technique is to predict tendencies in the spreading distribution...

Rock burst is one of the common failures in hard rock mining and civil construction. This study focuses on the prediction of rock burst classification with case instances using cloud models and attribution weight. First, cloud models are introduced briefly related to the rock burst...