APPLYING THE ARTIFICIAL NEURAL NETWORK METHODOLOGY FOR FORECASTING THE TOURISM TIME SERIES

a period of seven weeks of resistive exercise program by performing the movements of squatting and dead lift 65% of maximum capacity, three sets of 8-10 repetitions,

In this way, with this paper it is intended to present and discuss several alternative architectures of Artificial Neural Network models used to predict the time series of

Keywords: Artificial Neural Networks; Backpropagation; Box-Jenkins Methodology; Time Series Forecasting; Tourism Demand.. Currently available in the field of forecasting

This paper describes the process of modelling tourism demand for the north of Portugal, using an artificial neural network model. Data used in the time series was

Both series were separate into a training data set to train the neural network, in a validation set, to stop the training process earlier and a test data set to examine

Finally, in Figure 3, we consider the FPM threshold impacts on health spending in considering our LATE sample: cities near the thresholds in the time between 2002 and 2006,

Methodological advances developed to support the external validation of this neural network with clinical data include: imputation of missing data in both the training and

Todo este trabalho culminou na criação duma clutch inspirada no traje típico de Nisa mas ao mesmo tempo, tende a construir a possibilidade de, futuramente, criar esta e outras peças