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

Oscar Claveria, Enric Monte () and Salvador Torra ()
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Enric Monte: Department of Signal Theory and Communications. Polytechnic University of Catalunya.
Salvador Torra: Department of Econometrics & Riskcenter-IREA. Universitat de Barcelona

No 201503, AQR Working Papers from University of Barcelona, Regional Quantitative Analysis Group

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.

Keywords: Artificial neural networks; forecasting; multiple-input multiple-output (MIMO); seasonality; detrending; tourism demand; multilayer perceptron; radial basis function; Elman JEL classification: L83; C53; C45; R11 (search for similar items in EconPapers)
Pages: 16 pages
Date: 2015-01, Revised 2015-01
New Economics Papers: this item is included in nep-cmp, nep-ets, nep-for, nep-ore and nep-tur
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Working Paper: Effects of removing the trend and the seasonal component on the forecasting performance of artificial neural network techniques (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:aqr:wpaper:201503

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