{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T05:51:22Z","timestamp":1774677082333,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T00:00:00Z","timestamp":1745193600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Universidad de Buenos Aires","award":["UBACYT-20020220400162BA"],"award-info":[{"award-number":["UBACYT-20020220400162BA"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>This paper investigates the temporal evolution of cryptocurrency time series using information measures such as complexity, entropy, and Fisher information. The main objective is to differentiate between various levels of randomness and chaos. The methodology was applied to 176 daily closing price time series of different cryptocurrencies, from October 2015 to October 2024, with more than 30 days of data and not completely null. Complexity\u2013entropy causality plane (CECP) analysis reveals that daily cryptocurrency series with lengths of two years or less exhibit chaotic behavior, while those longer than two years display stochastic behavior. Most longer series resemble colored noise, with the parameter k varying between 0 and 2. Additionally, Natural Language Processing (NLP) analysis identified the most relevant terms in each white paper, facilitating a clustering method that resulted in four distinct clusters. However, no significant characteristics were found across these clusters in terms of the dynamics of the time series. This finding challenges the assumption that project narratives dictate market behavior. For this reason, investment recommendations should prioritize real-time informational metrics over whitepaper content.<\/jats:p>","DOI":"10.3390\/e27040450","type":"journal-article","created":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T20:42:00Z","timestamp":1745268120000},"page":"450","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Information Theory Quantifiers in Cryptocurrency Time Series Analysis"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-0649-7332","authenticated-orcid":false,"given":"Micaela","family":"Suriano","sequence":"first","affiliation":[{"name":"Departamento de Hidr\u00e1ulica, Facultad de Ingenier\u00eda, Universidad de Buenos Aires, Av. Las Heras 2214, Buenos Aires C1127AAR, Argentina"},{"name":"Laboratorio de Redes y Sistemas M\u00f3viles, Departamento de Electr\u00f3nica, Facultad de Ingenier\u00eda, Universidad de Buenos Aires, Buenos Aires C1063ACV, Argentina"}]},{"given":"Leonidas Facundo","family":"Caram","sequence":"additional","affiliation":[{"name":"Laboratorio de Redes y Sistemas M\u00f3viles, Departamento de Electr\u00f3nica, Facultad de Ingenier\u00eda, Universidad de Buenos Aires, Buenos Aires C1063ACV, Argentina"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5437-6095","authenticated-orcid":false,"given":"Cesar","family":"Caiafa","sequence":"additional","affiliation":[{"name":"Instituto Argentino de Radioastronom\u00eda-CCT La Plata, CONICET\/CIC-PBA\/UNLP, Camino Gral. Belgrano Km 40, Berazategui B1894XAB, Argentina"}]},{"given":"Hern\u00e1n Daniel","family":"Merlino","sequence":"additional","affiliation":[{"name":"Grupo IngenIA, Facultad de Ingenier\u00eda, Universidad de Buenos Aires, Buenos Aires C1063ACV, Argentina"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1288-2528","authenticated-orcid":false,"given":"Osvaldo Anibal","family":"Rosso","sequence":"additional","affiliation":[{"name":"Instituto de F\u00edsica (IFLP), Universidad Nacional de La Plata, CONICET, La Plata B1900AJJ, Argentina"},{"name":"Instituto de F\u00edsica, Universidade Federal de Alagoas (UFAL), Macei\u00f3 57072-970, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Dro\u017cd\u017c, S., Kwapie\u0144, J., O\u015bwi\u0119cimka, P., Stanisz, T., and W\u0105torek, M. (2020). Complexity in Economic and Social Systems: Cryptocurrency Market at around COVID-19. 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