Dynamic Time Warping: Intertemporal Clustering Alignments for Hotel Tourism Demand

Abstract

The consideration of the study on dynamic cluster flows in international tourists is an aspect that has been scarcely addressed in research despite its importance in economic development. Dynamic Time Warping is the methodology applied to identify alignments of common patterns in hotel demand time series within applied economics. The automatic determination of the number of clusters proposes an optimal number of groups for tourist destinations, and this proposition is confirmed through internal validation. Similarities among time series, including identifying outliers through boxplots, have been identified through the applied methodology. It has been employed for the primary tourist destinations in Spain for 106 international hotel demand time series. The effects of COVID-19 on the tourism sector and temporal similarities have been observed through clustering. The results that have been obtained reveal international tourist market flows that go beyond traditional analyses of seasonality or climatic factors, thus constituting a valuable tool for economic analysis in both direct and indirect markets.