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Article

Version

Accepted version

Publication date

2016

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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/68886

Combination forecasts of tourism demand with machine learning models

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Related resource

http://dx.doi.org/10.1080/13504851.2015.1078441

Abstract

The main objective of this study is to analyse whether the combination of regional predictions generated with machine learning (ML) models leads to improved forecast accuracy. With this aim we construct one set of forecasts by estimating models on the aggregate series, another set by using the same models to forecast the individual series prior to aggregation, and then we compare the accuracy of both approaches. We use three ML techniques: Support Vector Regression (SVR), Gaussian Process Regression (GPR) and Neural Network (NN) models. We use an ARMA model as a benchmark. We find that ML methods improve their forecasting performance with respect to the benchmark as forecast horizons increase, suggesting the suitability of these techniques for mid- and long-term forecasting. In spite of the fact that the disaggregated approach yields more accurate predictions, the improvement over the benchmark occurs for shorter forecast horizons with the direct approach.

Subject

Previsió, Xarxes neuronals (Informàtica), Distribució de Gauss, Anàlisi de regressió, Aprenentatge automàtic

Subject (English)

Forecasting, Neural networks (Computer science), Gaussian distribution, Regression analysis, Machine learning

Citation

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Articles publicats en revistes (Econometria, Estadística i Economia Aplicada)

Citation

CLAVERÍA GONZÁLEZ, Óscar, MONTE MORENO, Enric and TORRA PORRAS, Salvador. Combination forecasts of tourism demand with machine learning models. Applied Economics Letters. 2016. Vol. 23, num. 6, pags. 428-431. ISSN 1350-4851. [consulted: 13 of August of 2026]. Available at: https://hdl.handle.net/2445/68886

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