What is it about?

In this study we combine the results of two independent analyses to position Spanish regions according to both the characteristics of the time series of international tourist arrivals and the accuracy of predictions of arrivals at the regional level. We apply a seasonal-trend decomposition procedure based on non-parametric regression to isolate the different components of the series and calculate the main time series features. Predictions are generated with several machine learning models in a recursive multi-step-ahead forecasting experiment. Finally, we summarize all the information from the two previous experiments using categorical principal component analysis.

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Why is it important?

By overlapping the distribution of the regions and the component loadings of each variable along both dimensions, we observe that entropy and dispersion show an inverse relation with forecast accuracy, but the interactions between the rest of the features and accuracy are heavily dependent on the forecast horizon. On this evidence, we conclude that in order to increase forecast accuracy of tourist arrivals at the regional level, model selection should be region-specific and based on the forecast horizon.

Perspectives

The anticipation of tourist demand at a regional level is of vital importance for the sector. In the current context of growing uncertainty, machine learning methods take on an increasingly prominent role in prediction. By evaluating the role time series features on forecast accuracy at a regional level, we provide evidence that in order to increase forecast accuracy of tourist arrivals, model selection should be region-specific and based on the forecast horizon.

Oscar Claveria
AQR-IREA, Univeristy of Barcelona

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This page is a summary of: Time series features and machine learning forecasts, Tourism Analysis, January 2020, Cognizant Communication Corporation, DOI: 10.3727/108354220x16002732379690.
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