Effects of removing the trend and the seasonal component on the forecasting performance of artificial neural network techniques
Visualitza/Obre
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/192727
Tipus de documentReport de recerca
Data publicació2015-07-02
Condicions d'accésAccés obert
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
This study aims to analyze the effects of data pre-processing on the performance of forecasting based on neural network models. We use three different Artificial Neural Networks techniques to forecast tourist demand: a multi-layer perceptron, a radial basis function and an Elman neural network. The structure of the networks is based on a multiple-input multiple-output setting (i.e. all countries are forecasted simultaneously). We use official statistical data of inbound international tourism demand to Catalonia (Spain) and compare the forecasting accuracy of four processing methods for the input vector of the networks: levels, growth rates, seasonally adjusted levels and seasonally adjusted growth rates. When comparing the forecasting accuracy of the different inputs for each visitor market and for different forecasting horizons, we obtain significantly better forecasts with levels than with growth rates. We also find that seasonally adjusted series significantly improve the forecasting performance of the networks, which hints at the significance of deseasonalizing the time series when using neural networks with forecasting purposes. These results reveal that, when using seasonal data, neural networks performance can be significantly improved by working directly with seasonally adjusted levels.
Descripció
Working paper
CitacióClaveria, O.; Monte, E.; Torra Porras, S. "Effects of removing the trend and the seasonal component on the forecasting performance of artificial neural network techniques". 2015.
URL repositori externhttp://www.ub.edu/irea/working_papers/2015/201503.pdf
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Effects of removing.pdf | 462,5Kb | Visualitza/Obre |