{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T19:18:23Z","timestamp":1773515903429,"version":"3.50.1"},"reference-count":67,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2017,7,29]],"date-time":"2017-07-29T00:00:00Z","timestamp":1501286400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Geophysical time series have a complex nature that poses challenges to reaching assertive conclusions, and require advanced mathematical and computational tools to unravel embedded information. In this paper, time\u2013frequency methods and hierarchical clustering (HC) techniques are combined for processing and visualizing tidal information. In a first phase, the raw data are pre-processed for estimating missing values and obtaining dimensionless reliable time series. In a second phase, the Jensen\u2013Shannon divergence is adopted for measuring dissimilarities between data collected at several stations. The signals are compared in the frequency and time\u2013frequency domains, and the HC is applied to visualize hidden relationships. In a third phase, the long-range behavior of tides is studied by means of power law functions. Numerical examples demonstrate the effectiveness of the approach when dealing with a large volume of real-world data.<\/jats:p>","DOI":"10.3390\/e19080390","type":"journal-article","created":{"date-parts":[[2017,8,1]],"date-time":"2017-08-01T03:30:06Z","timestamp":1501558206000},"page":"390","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Tidal Analysis Using Time\u2013Frequency Signal Processing and Information Clustering"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7359-4370","authenticated-orcid":false,"given":"Antonio","family":"M. Lopes","sequence":"first","affiliation":[{"name":"UISPA\u2013LAETA\/INEGI, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias,4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4274-4879","authenticated-orcid":false,"given":"Jose","family":"Tenreiro Machado","sequence":"additional","affiliation":[{"name":"Institute of Engineering, Polytechnic of Porto, Department of Electrical Engineering,Rua Dr. Ant\u00f3nio Bernardino de Almeida, 431, 4249-015 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2017,7,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Takens, F. (1981). Detecting Strange Attractors in Turbulence. 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