{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T00:19:48Z","timestamp":1770337188707,"version":"3.49.0"},"reference-count":35,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T00:00:00Z","timestamp":1691366400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Special Program of Guangxi Science and Technology Base and Talent","award":["AD21220098"],"award-info":[{"award-number":["AD21220098"]}]},{"name":"the Special Program of Guangxi Science and Technology Base and Talent","award":["AD21220098"],"award-info":[{"award-number":["AD21220098"]}]},{"name":"2021 Open Fund project of the Key Laboratory of Cognitive Radio and Information Processing of the Ministry of Education, and the Special Program of Guangxi Science and Technology Base and Talent","award":["AD21220098"],"award-info":[{"award-number":["AD21220098"]}]},{"name":"2021 Open Fund project of the Key Laboratory of Cognitive Radio and Information Processing of the Ministry of Education, and the Special Program of Guangxi Science and Technology Base and Talent","award":["AD21220098"],"award-info":[{"award-number":["AD21220098"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Entropy quantification approaches have gained considerable attention in engineering applications. However, certain limitations persist, including the strong dependence on parameter selection, limited discriminating power, and low robustness to noise. To alleviate these issues, this paper introduces two novel algorithms for time series analysis: the ensemble improved permutation entropy (EIPE) and multiscale EIPE (MEIPE). Our approaches employ a new symbolization process that considers both permutation relations and amplitude information. Additionally, the ensemble technique is utilized to reduce the dependence on parameter selection. We performed a comprehensive evaluation of the proposed methods using various synthetic and experimental signals. The results illustrate that EIPE is capable of distinguishing white, pink, and brown noise with a smaller number of samples compared to traditional entropy algorithms. Furthermore, EIPE displays the potential to discriminate between regular and non-regular dynamics. Notably, when compared to permutation entropy, weighted permutation entropy, and dispersion entropy, EIPE exhibits superior robustness against noise. In practical applications, such as RR interval data classification, bearing fault diagnosis, marine vessel identification, and electroencephalographic (EEG) signal classification, the proposed methods demonstrate better discriminating power compared to conventional entropy measures. These promising findings validate the effectiveness and potential of the algorithms proposed in this paper.<\/jats:p>","DOI":"10.3390\/e25081175","type":"journal-article","created":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T06:37:31Z","timestamp":1691390251000},"page":"1175","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Ensemble Improved Permutation Entropy: A New Approach for Time Series Analysis"],"prefix":"10.3390","volume":"25","author":[{"given":"Zhe","family":"Chen","sequence":"first","affiliation":[{"name":"School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China"},{"name":"Key Lab. of Cognitive Radio & Information Processing, The Ministry of Education, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaodong","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China"},{"name":"Key Lab. of Cognitive Radio & Information Processing, The Ministry of Education, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3462-8897","authenticated-orcid":false,"given":"Jielin","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China"},{"name":"Key Lab. of Cognitive Radio & Information Processing, The Ministry of Education, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5973-9780","authenticated-orcid":false,"given":"Yaan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Marine Science and Technology, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1403829","DOI":"10.1155\/2019\/1403829","article-title":"Improved permutation entropy for measuring complexity of time series under noisy condition","volume":"2019","author":"Chen","year":"2019","journal-title":"Complexity"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"112101","DOI":"10.1016\/j.chaos.2022.112101","article-title":"An improved multiscale distribution entropy for analyzing complexity of real-world signals","volume":"158","author":"Deka","year":"2022","journal-title":"Chaos Solitons Fractals"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/0167-2789(85)90011-9","article-title":"Determining Lyapunov exponents from a time series","volume":"16","author":"Wolf","year":"1985","journal-title":"Phys. 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