Mostra el registre d'ítem simple
Effects of removing the trend and the seasonal component on the forecasting performance of artificial neural network techniques
dc.contributor.author | Claveria González, Oscar |
dc.contributor.author | Monte Moreno, Enrique |
dc.contributor.author | Torra Porras, Salvador |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions |
dc.date.accessioned | 2020-07-09T11:31:54Z |
dc.date.available | 2020-07-09T11:31:54Z |
dc.date.issued | 2015-07-02 |
dc.identifier.citation | Claveria, 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. |
dc.identifier.uri | http://hdl.handle.net/2117/192727 |
dc.description | Working paper |
dc.description.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. |
dc.format.extent | 16 p. |
dc.language.iso | eng |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
dc.subject | Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica |
dc.subject.lcsh | Neural networks (Computer science) |
dc.subject.lcsh | Economic forecasting |
dc.subject.other | Artificial neural network |
dc.subject.other | Tourist forecasting |
dc.subject.other | Elman neural networks |
dc.subject.other | Back propagation |
dc.subject.other | Radial basis |
dc.subject.other | Multilayer neural network |
dc.title | Effects of removing the trend and the seasonal component on the forecasting performance of artificial neural network techniques |
dc.type | External research report |
dc.subject.lemac | Xarxes neuronals (Informàtica) |
dc.subject.lemac | Previsió econòmica |
dc.contributor.group | Universitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla |
dc.relation.publisherversion | http://www.ub.edu/irea/working_papers/2015/201503.pdf |
dc.rights.access | Open Access |
local.identifier.drac | 28852256 |
dc.description.version | Preprint |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO//TEC2012-38939-C03-02/ES/INVESTIGACION AVANZADA EN TECNOLOGIAS DEL HABLA PARA APLICACION A ENTORNOS AUDIOVISUALES/ |
local.citation.author | Claveria, O.; Monte, E.; Torra Porras, Salvador |
Fitxers d'aquest items
Aquest ítem apareix a les col·leccions següents
-
Reports de recerca [42]
-
Reports de recerca [198]