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Effects of removing the trend and the seasonal component on the forecasting performance of artificial neural network techniques

By Óscar Clavería González, Enric Monte Moreno and Salvador Torra Porras

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

Topics: Politics of tourism, MIMO systems, Data transmission systems, Economic development, Política turística, Xarxes neuronals (Informàtica), Sistemes MIMO, Transmissió de dades, Desenvolupament econòmic
Publisher: Universitat de Barcelona. Institut de Recerca en Economia Aplicada Regional i Pública
Year: 2015
OAI identifier: oai:diposit.ub.edu:2445/61328
Journal:

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