{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T10:26:39Z","timestamp":1758709599536,"version":"3.37.3"},"reference-count":55,"publisher":"Wiley","license":[{"start":{"date-parts":[[2018,8,9]],"date-time":"2018-08-09T00:00:00Z","timestamp":1533772800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Education and Training Vietnam International Education Development","award":["CPER 2014-2020 MARCO"],"award-info":[{"award-number":["CPER 2014-2020 MARCO"]}]},{"name":"French government","award":["CPER 2014-2020 MARCO"],"award-info":[{"award-number":["CPER 2014-2020 MARCO"]}]},{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","award":["CPER 2014-2020 MARCO"],"award-info":[{"award-number":["CPER 2014-2020 MARCO"]}],"id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Computational Intelligence and Soft Computing"],"published-print":{"date-parts":[[2018,8,9]]},"abstract":"<jats:p>The completion of missing values is a prevalent problem in many domains of pattern recognition and signal processing. Analyzing data with incompleteness may lead to a loss of power and unreliable results, especially for large missing subsequence(s). Therefore, this paper aims to introduce a new approach for filling successive missing values in low\/uncorrelated multivariate time series which allows managing a high level of uncertainty. In this way, we propose using a novel fuzzy weighting-based similarity measure. The proposed method involves three main steps. Firstly, for each incomplete signal, the data before a gap and the data after this gap are considered as two separated reference time series with their respective query windows<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><mml:mi>Q<\/mml:mi><mml:mi>b<\/mml:mi><\/mml:math>and<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\"><mml:mi>Q<\/mml:mi><mml:mi>a<\/mml:mi><\/mml:math>. We then find the most similar subsequence (<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M3\"><mml:mi>Q<\/mml:mi><mml:mi>b<\/mml:mi><mml:mi>s<\/mml:mi><\/mml:math>) to the subsequence before this gap<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M4\"><mml:mi>Q<\/mml:mi><mml:mi>b<\/mml:mi><\/mml:math>and the most similar one (<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M5\"><mml:mi>Q<\/mml:mi><mml:mi>a<\/mml:mi><mml:mi>s<\/mml:mi><\/mml:math>) to the subsequence after the gap<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M6\"><mml:mi>Q<\/mml:mi><mml:mi>a<\/mml:mi><\/mml:math>. To find these similar windows, we build a new similarity measure based on fuzzy grades of basic similarity measures and on fuzzy logic rules. Finally, we fill in the gap with average values of the window following<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M7\"><mml:mi>Q<\/mml:mi><mml:mi>b<\/mml:mi><mml:mi>s<\/mml:mi><\/mml:math>and the one preceding<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M8\"><mml:mi>Q<\/mml:mi><mml:mi>a<\/mml:mi><mml:mi>s<\/mml:mi><\/mml:math>. The experimental results have demonstrated that the proposed approach outperforms the state-of-the-art methods in case of multivariate time series having low\/noncorrelated data but effective information on each signal.<\/jats:p>","DOI":"10.1155\/2018\/9095683","type":"journal-article","created":{"date-parts":[[2018,8,9]],"date-time":"2018-08-09T19:56:32Z","timestamp":1533844592000},"page":"1-15","source":"Crossref","is-referenced-by-count":9,"title":["A New Fuzzy Logic-Based Similarity Measure Applied to Large Gap Imputation for Uncorrelated Multivariate Time Series"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6880-3721","authenticated-orcid":true,"given":"Thi-Thu-Hong","family":"Phan","sequence":"first","affiliation":[{"name":"Univ. 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