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Combination forecasts of tourism demand with machine learning models

Author
Claveria, O.; Monte, E.; Torra Porras, Salvador
Type of activity
Journal article
Journal
Applied economics letters
Date of publication
2015-09-11
Volume
23
Number
6
First page
428
Last page
431
DOI
https://doi.org/10.1080/13504851.2015.1078441 Open in new window
Repository
http://hdl.handle.net/2117/83765 Open in new window
URL
http://www.tandfonline.com/doi/abs/10.1080/13504851.2015.1078441?journalCode=rael20 Open in new window
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, Gaussian process regression ...
Citation
Claveria, O., Monte, E., Torra, S. Combination forecasts of tourism demand with machine learning models. "Applied economics letters", 11 Setembre 2015, vol. 23, núm. 6, p. 428-431.
Keywords
Forecast combination, Gaussian process regression, machine learning, neural networks, support vector regression
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
TALP - Centre for Language and Speech Technologies and Applications
VEU - Speech Processing Group

Participants

  • Claveria González, Oscar  (author)
  • Monte Moreno, Enrique  (author)
  • Torra Porras, Salvador  (author)