Dynamic Time Warping: Intertemporal Clustering Alignments for Hotel Tourism Demand

Ruiz Reina, Miguel Ángel

doi:10.1007/s10614-024-10656-8

Comput Econ

2024

DOI: 10.1007/s10614-024-10656-8

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Dynamic Time Warping: Intertemporal Clustering Alignments for Hotel Tourism Demand

Miguel Ángel Ruiz Reina

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2024

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Spatio-temporal hierarchical clustering of interval time series with application to suicide rates in Europe

Mattera,

Franses

2024

Statistical Modelling

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In this paper, we investigate similarities of suicide rates in Europe, which are available as interval time series. For this aim, a novel spatio-temporal hierarchical clustering algorithm for interval time-series data is proposed. The spatial dimension is included in the clustering process to account for possible relevant information such as weather conditions, sunlight hours and socio-cultural factors. Our results indicate the presence of six main clusters in Europe, which almost overlap with the sunlight hours distribution. Differences between male and female suicide rates are also investigated.

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Spatio-temporal hierarchical clustering of interval time series with application to suicide rates in Europe

Mattera,

Franses

2024

Statistical Modelling

View full text Add to dashboard Cite

show abstract

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Dynamic Time Warping: Intertemporal Clustering Alignments for Hotel Tourism Demand

Cited by 1 publication

References 30 publications

Spatio-temporal hierarchical clustering of interval time series with application to suicide rates in Europe

Spatio-temporal hierarchical clustering of interval time series with application to suicide rates in Europe

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