{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T09:04:49Z","timestamp":1785315889490,"version":"3.55.0"},"reference-count":27,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T00:00:00Z","timestamp":1781049600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["SoftwareX"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.softx.2026.102809","type":"journal-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T22:06:49Z","timestamp":1781647609000},"page":"102809","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["uniTS-MissRecoPred: framework for degradation and reconstruction of univariate time series with missing values to improve forecasting effectiveness"],"prefix":"10.1016","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7904-786X","authenticated-orcid":false,"given":"Dariusz","family":"Kobiela","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7877-9189","authenticated-orcid":false,"given":"Jaros\u0142aw","family":"Kobiela","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5132-3016","authenticated-orcid":false,"given":"Adam","family":"Kurowski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4728-689X","authenticated-orcid":false,"given":"Agnieszka","family":"Landowska","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.softx.2026.102809_bib0005","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1007\/s00259-023-06417-8","article-title":"PET image denoising based on denoising diffusion probabilistic model","volume":"51","author":"Gong","year":"2024","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"10.1016\/j.softx.2026.102809_bib0010","series-title":"Essential statistical methods for medical statistics","first-page":"235","article-title":"8 - Statistical modeling in biomedical research: longitudinal data analysis","author":"Xiong","year":"2011"},{"key":"10.1016\/j.softx.2026.102809_bib0015","author":"Du"},{"key":"10.1016\/j.softx.2026.102809_bib0020","author":"Nater"},{"key":"10.1016\/j.softx.2026.102809_bib0025","series-title":"pandas-dev\/pandas: pandas","year":"2020"},{"issue":"85","key":"10.1016\/j.softx.2026.102809_bib0030","first-page":"2825","article-title":"Scikit-Learn: machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"10.1016\/j.softx.2026.102809_bib0035","series-title":"SciPy 2010","article-title":"Statsmodels: econometric and statistical modeling with Python","author":"Seabold","year":"2010"},{"key":"10.1016\/j.softx.2026.102809_bib0040","author":"L\u00f6ning"},{"issue":"1","key":"10.1016\/j.softx.2026.102809_bib0045","doi-asserted-by":"crossref","first-page":"5389","DOI":"10.1038\/s41598-025-86382-4","article-title":"Hinge-FM2I: an approach using image inpainting for interpolating missing data in univariate time series","volume":"15","author":"Noufel","year":"2025","journal-title":"Sci Rep"},{"key":"10.1016\/j.softx.2026.102809_bib0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.softx.2025.102484","article-title":"HySim-IRIS: hybrid similarity interactive restoration and inpainting suite","volume":"33","author":"Noufel","year":"2026","journal-title":"Softwarex"},{"key":"10.1016\/j.softx.2026.102809_bib0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.rineng.2025.106871","article-title":"HySim: an efficient hybrid similarity measure for patch matching in image inpainting","volume":"27","author":"Noufel","year":"2025","journal-title":"Results Eng"},{"key":"10.1016\/j.softx.2026.102809_bib0060","series-title":"Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining","first-page":"2623","article-title":"Optuna: a next-generation hyperparameter optimization framework","author":"Akiba","year":"2019"},{"issue":"124","key":"10.1016\/j.softx.2026.102809_bib0065","first-page":"1","article-title":"Darts: user-friendly modern machine learning for time series","volume":"23","author":"Herzen","year":"2022","journal-title":"J Mach Learn Res"},{"key":"10.1016\/j.softx.2026.102809_bib0070","author":"Manaenkov"},{"key":"10.1016\/j.softx.2026.102809_bib0080","article-title":"Inpainting as reconstruction method of univariate time series with missing values to improve forecasting effectiveness","author":"Kobiela","year":"2025","journal-title":"Data Min Knowl Discov"},{"issue":"3","key":"10.1016\/j.softx.2026.102809_bib0085","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1093\/biomet\/63.3.581","article-title":"Inference and missing data","volume":"63","author":"Rubin","year":"1976","journal-title":"Biometrika"},{"issue":"5","key":"10.1016\/j.softx.2026.102809_bib0090","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1002\/bimj.202000196","article-title":"Missing data: a statistical framework for practice","volume":"63","author":"Carpenter","year":"2021","journal-title":"Biom J"},{"key":"10.1016\/j.softx.2026.102809_bib0095","author":"Zhou"},{"key":"10.1016\/j.softx.2026.102809_bib0100","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-018-24271-9","article-title":"Recurrent neural networks for multivariate time series with missing values","volume":"8","author":"Che","year":"2018","journal-title":"Sci Rep"},{"key":"10.1016\/j.softx.2026.102809_bib0105","series-title":"Proceedings of the 32nd international conference on neural information processing systems, NIPS\u201918","first-page":"6776","article-title":"BRITS: bidirectional recurrent imputation for time series","author":"Cao","year":"2018"},{"issue":"C","key":"10.1016\/j.softx.2026.102809_bib0110","article-title":"SAITS: self-attention-based imputation for time series","volume":"219","author":"Du","year":"2023","journal-title":"Expert Syst Appl"},{"key":"10.1016\/j.softx.2026.102809_bib0115","series-title":"Proceedings of the 35th international conference on neural information processing systems, NIPS \u201921","article-title":"CSDI: conditional score-based diffusion models for probabilistic time series imputation","author":"Tashiro","year":"2021"},{"key":"10.1016\/j.softx.2026.102809_bib0120","author":"Du"},{"key":"10.1016\/j.softx.2026.102809_bib0125","series-title":"Proceedings of the 24th international conference on artificial intelligence, IJCAI\u201915","first-page":"3939","article-title":"Imaging time-series to improve classification and imputation","author":"Wang","year":"2015"},{"issue":"9","key":"10.1016\/j.softx.2026.102809_bib0130","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1209\/0295-5075\/4\/9\/004","article-title":"Recurrence plots of dynamical systems","volume":"4","author":"Eckmann","year":"1987","journal-title":"Europhys. Lett."},{"key":"10.1016\/j.softx.2026.102809_bib0135","series-title":"2022 IEEE\/CVF conference on computer vision and pattern recognition (CVPR)","first-page":"10674","article-title":"High-resolution image synthesis with latent diffusion models","author":"Rombach","year":"2022"},{"key":"10.1016\/j.softx.2026.102809_bib0140","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbi.2023.104440","article-title":"Deep imputation of missing values in time series health data: a review with benchmarking","volume":"144","author":"Kazijevs","year":"2023","journal-title":"J Biomed Inform"}],"container-title":["SoftwareX"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2352711026003018?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2352711026003018?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T08:52:58Z","timestamp":1785315178000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2352711026003018"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":27,"alternative-id":["S2352711026003018"],"URL":"https:\/\/doi.org\/10.1016\/j.softx.2026.102809","relation":{},"ISSN":["2352-7110"],"issn-type":[{"value":"2352-7110","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"uniTS-MissRecoPred: framework for degradation and reconstruction of univariate time series with missing values to improve forecasting effectiveness","name":"articletitle","label":"Article Title"},{"value":"SoftwareX","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.softx.2026.102809","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"102809"}}