Barbosa, S. M.; Gouveia, S.; Scotto, M. G.; Alonso, A. M. Wavelet-based clustering of sea level records. (English) Zbl 1397.86022 Math. Geosci. 48, No. 2, 149-162 (2016). Summary: The classification of multivariate time series in terms of their corresponding temporal dependence patterns is a common problem in geosciences, particularly for large datasets resulting from environmental monitoring networks. Here a wavelet-based clustering approach is applied to sea level and atmospheric pressure time series at tide gauge locations in the Baltic Sea. The resulting dendrogram discriminates three spatially-coherent groups of stations separating the southernmost tide gauges, reflecting mainly high-frequency variability driven by zonal wind, from the middle-basin stations and the northernmost stations dominated by lower-frequency variability and the response to atmospheric pressure. MSC: 86A32 Geostatistics 86A05 Hydrology, hydrography, oceanography 86A10 Meteorology and atmospheric physics 62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH) Keywords:multivariate time series; enviromental monitoring networks; sea level and atmospheric pressure time series Software:impute; wmtsa PDFBibTeX XMLCite \textit{S. M. Barbosa} et al., Math. 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