A regional perspective on the accuracy of machine learning forecasts of tourism demand based on data characteristics
Oscar Claveria (),
Enric Monte and
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Enric Monte: Polytechnic University of Catalunya (UPC)
Salvador Torra: Riskcenter-IREA, University of Barcelona
No 201802, AQR Working Papers from University of Barcelona, Regional Quantitative Analysis Group
In this work we assess the role of data characteristics in the accuracy of machine learning (ML) tourism forecasts from a spatial perspective. First, we apply a seasonal-trend decomposition procedure based on non-parametric regression to isolate the different components of the time series of international tourism demand to all Spanish regions. This approach allows us to compute a set of measures to describe the features of the data. Second, we analyse the performance of several ML models in a recursive multiple-step-ahead forecasting experiment. In a third step, we rank all seventeen regions according to their characteristics and the obtained forecasting performance, and use the rankings as the input for a multivariate analysis to evaluate the interactions between time series features and the accuracy of the predictions. By means of dimensionality reduction techniques we summarise all the information into two components and project all Spanish regions into perceptual maps. We find that entropy and dispersion show a negative relation with accuracy, while the effect of other data characteristics on forecast accuracy is heavily dependent on the forecast horizon.
Keywords: STL decomposition; non-parametric regression; time series features; forecast accuracy; machine learning; tourism demand; regional analysis JEL classification: C45; C51; C53; C63; E27; L83 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big, nep-for, nep-tur and nep-ure
Date: 2018-04, Revised 2018-04
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Working Paper: A regional perspective on the accuracy of machine learning forecasts of tourism demand based on data characteristics (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:aqr:wpaper:201802
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