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

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.