{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T15:01:44Z","timestamp":1788361304332,"version":"build-2803163510"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2019,11,15]],"date-time":"2019-11-15T00:00:00Z","timestamp":1573776000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,11,15]],"date-time":"2019-11-15T00:00:00Z","timestamp":1573776000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["732328"],"award-info":[{"award-number":["732328"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2020,6]]},"DOI":"10.1007\/s10115-019-01421-7","type":"journal-article","created":{"date-parts":[[2019,11,15]],"date-time":"2019-11-15T21:02:41Z","timestamp":1573851761000},"page":"2257-2280","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Scalable recovery of missing blocks in time series with high and low cross-correlations"],"prefix":"10.1007","volume":"62","author":[{"given":"Mourad","family":"Khayati","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philippe","family":"Cudr\u00e9-Mauroux","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael H.","family":"B\u00f6hlen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,11,15]]},"reference":[{"key":"1421_CR1","unstructured":"Administration CM (2018) Temperature TS. https:\/\/www.hydrodaten.admin.ch\/en. Accessed 01 July 2018"},{"issue":"3","key":"1421_CR2","doi-asserted-by":"publisher","first-page":"40:1","DOI":"10.1145\/3287319","volume":"2","author":"A Agarwal","year":"2018","unstructured":"Agarwal A, Amjad MJ, Shah D, Shen D (2018) Model agnostic time series analysis via matrix estimation. POMACS 2(3):40:1\u201340:39. https:\/\/doi.org\/10.1145\/3287319","journal-title":"POMACS"},{"key":"1421_CR3","unstructured":"Agarwal A, Amjad MJ, Shah D, Shen D (2018) Time series analysis via matrix estimation. CoRR arXiv:1802.09064"},{"issue":"18","key":"1421_CR4","doi-asserted-by":"publisher","first-page":"10101","DOI":"10.1073\/pnas.97.18.10101","volume":"97","author":"O Alter","year":"2000","unstructured":"Alter O, Brown PO, Botstein D (2000) Singular value decomposition for genome-wide expression data processing and modeling. Proc Natl Acad Sci USA 97(18):10101\u20136","journal-title":"Proc Natl Acad Sci USA"},{"key":"1421_CR5","doi-asserted-by":"crossref","unstructured":"Balzano L, Chi Y, Lu YM (2018) Streaming PCA and subspace tracking: the missing data case. In: Proceedings of the IEEE 106(7)","DOI":"10.1109\/JPROC.2018.2847041"},{"key":"1421_CR6","doi-asserted-by":"publisher","unstructured":"Balzano L, Nowak R, Recht B (2010) Online identification and tracking of subspaces from highly incomplete information. In: 2010 48th Annual allerton conference on communication, control, and computing (Allerton). https:\/\/doi.org\/10.1109\/allerton.2010.5706976","DOI":"10.1109\/allerton.2010.5706976"},{"key":"1421_CR7","unstructured":"Bodik P, Hong W, Guestrin C, Madden S, Paskin M, Thibaux R (2004) Intel Berkeley research lab dataset. http:\/\/db.csail.mit.edu\/labdata\/labdata.html. Accessed 8 Nov 2018"},{"key":"1421_CR8","doi-asserted-by":"publisher","unstructured":"Cambronero J, Feser JK, Smith MJ, Madden S (2017) Query optimization for dynamic imputation. PVLDB 10(11):1310\u20131321. https:\/\/doi.org\/10.14778\/3137628.3137641. http:\/\/www.vldb.org\/pvldb\/vol10\/p1310-feser.pdf","DOI":"10.14778\/3137628.3137641"},{"issue":"4","key":"1421_CR9","doi-asserted-by":"publisher","first-page":"1025","DOI":"10.1137\/S0895479800382555","volume":"23","author":"MT Chu","year":"2002","unstructured":"Chu MT, Funderlic R (2002) The centroid decomposition: relationships between discrete variational decompositions and svds. SIAM J Matrix Anal Appl 23(4):1025\u20131044. https:\/\/doi.org\/10.1137\/S0895479800382555","journal-title":"SIAM J Matrix Anal Appl"},{"key":"1421_CR10","doi-asserted-by":"publisher","unstructured":"D\u2019agostino Sr, RB, Russell HK (2005) Centroid method. Wiley. https:\/\/doi.org\/10.1002\/0470011815.b2a13006","DOI":"10.1002\/0470011815.b2a13006"},{"key":"1421_CR11","unstructured":"Dheeru D, Karra TE (2017) UCI machine learning repository. http:\/\/archive.ics.uci.edu\/ml. Accessed 1 Jan 2019"},{"key":"1421_CR12","unstructured":"for\u00a0the Environment\u00a0FOEN, F.O.: BAFU TS. https:\/\/www.meteoswiss.admin.ch\/home.html?tab=alarm. Accessed 01 Mar 2018"},{"issue":"3","key":"1421_CR13","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1207\/S15328007SEM0703_1","volume":"7","author":"MS Gold","year":"2000","unstructured":"Gold MS, Bentler PM (2000) Treatments of missing data: a monte carlo comparison of rbhdi, iterative stochastic regression imputation, and expectation-maximization. Struct Equ Model Multidiscip J 7(3):319\u2013355. https:\/\/doi.org\/10.1207\/S15328007SEM0703_1","journal-title":"Struct Equ Model Multidiscip J"},{"key":"1421_CR14","unstructured":"Hening D, Koonce DA (2014) Missing data imputation method comparison in Ohio university student retention database. In: Proceedings of the 2014 international conference on industrial engineering and operations management, Bali, Indonesia, January 7\u20139, 2014"},{"issue":"12","key":"1421_CR15","doi-asserted-by":"publisher","first-page":"2270","DOI":"10.1016\/j.patcog.2005.01.012","volume":"38","author":"AK Jain","year":"2005","unstructured":"Jain AK, Nandakumar K, Ross A (2005) Score normalization in multimodal biometric systems. Pattern Recognit 38(12):2270\u20132285. https:\/\/doi.org\/10.1016\/j.patcog.2005.01.012","journal-title":"Pattern Recognit"},{"key":"1421_CR16","doi-asserted-by":"publisher","unstructured":"Khayati M, B\u00f6hlen MH, Cudr\u00e9-Mauroux P (2015) Using lowly correlated time series to recover missing values in time series: a comparison between SVD and CD. In: Advances in spatial and temporal databases\u201414th international symposium, SSTD 2015, Hong Kong, China, August 26\u201328, 2015. Proceedings, pp. 237\u2013254. https:\/\/doi.org\/10.1007\/978-3-319-22363-6_13","DOI":"10.1007\/978-3-319-22363-6_13"},{"key":"1421_CR17","doi-asserted-by":"crossref","unstructured":"Khayati M, B\u00f6hlen MH, Gamper J (2014) Memory-efficient centroid decomposition for long time series. In: IEEE 30th international conference on data engineering, Chicago, ICDE 2014, IL, USA, March 31\u2013April 4, 2014, pp 100\u2013111","DOI":"10.1109\/ICDE.2014.6816643"},{"key":"1421_CR18","doi-asserted-by":"crossref","unstructured":"Lee M, An J, Lee Y (2019) Missing-value imputation of continuous missing based on deep imputation network using correlations among multiple IOT data streams in a smart space. IEICE Trans 102-D(2):289\u2013298. http:\/\/search.ieice.org\/bin\/summary.php?id=e102-d_2_289","DOI":"10.1587\/transinf.2018EDP7257"},{"key":"1421_CR19","unstructured":"Mei J, de\u00a0Castro Y, Goude Y, H\u00e9brail G (2017) Nonnegative matrix factorization for time series recovery from a few temporal aggregates. In: Proceedings of the 34th international conference on machine learning, ICML 2017, Sydney, NSW, Australia, 6\u201311 August 2017, pp 2382\u20132390"},{"key":"1421_CR20","unstructured":"Meteorology FO (2018) Climatology: MeteoSwiss TS. https:\/\/www.hydrodaten.admin.ch\/en. Accessed 01 Mar 2018"},{"key":"1421_CR21","doi-asserted-by":"publisher","unstructured":"Meyer CD (2000) Matrix analysis and applied linear algebra. SIAM. https:\/\/doi.org\/10.1137\/1.9780898719512. https:\/\/my.siam.org\/Store\/Product\/viewproduct\/?ProductId=971","DOI":"10.1137\/1.9780898719512"},{"key":"1421_CR22","unstructured":"Moritz S, Sard\u00e1 A, Bartz-Beielstein T, Zaefferer M, Stork J (2015) Comparison of different methods for univariate time series imputation in R. CoRR arXiv:1510.03924"},{"key":"1421_CR23","unstructured":"Ongie G, Willett R, Nowak RD, Balzano L (2017) Algebraic variety models for high-rank matrix completion. In: Proceedings of the 34th international conference on machine learning, ICML 2017, Sydney, NSW, Australia, 6\u201311 August 2017, pp 2691\u20132700"},{"key":"1421_CR24","unstructured":"Rasp J (2018) Statistical quality control. http:\/\/www.cma.gov.cn. Accessed 01 July 2018"},{"key":"1421_CR25","first-page":"3413","volume":"12","author":"B Recht","year":"2011","unstructured":"Recht B (2011) A simpler approach to matrix completion. J Mach Learn Res 12:3413\u20133430","journal-title":"J Mach Learn Res"},{"key":"1421_CR26","doi-asserted-by":"publisher","unstructured":"Rodriguez-Lujan I, Fonollosa J, Vergara A, Homer M, Huerta R (2014) On the calibration of sensor arrays for pattern recognition using the minimal number of experiments. Chemom Intell Lab Syst 130:123\u2013134. https:\/\/doi.org\/10.1016\/j.chemolab.2013.10.012","DOI":"10.1016\/j.chemolab.2013.10.012"},{"key":"1421_CR27","doi-asserted-by":"publisher","unstructured":"Sanderson C, Curtin RR (2018) A user-friendly hybrid sparse matrix class in C++. In: Mathematical software\u2014ICMS 2018\u20146th international conference, South Bend, IN, USA, July 24\u201327, 2018, Proceedings, pp 422\u2013430. https:\/\/doi.org\/10.1007\/978-3-319-96418-8_50","DOI":"10.1007\/978-3-319-96418-8_50"},{"key":"1421_CR28","doi-asserted-by":"publisher","unstructured":"Vergara A, Vembu S, Ayhan T, Ryan MA, Homer ML, Huerta R (2012) Chemical gas sensor drift compensation using classifier ensembles. Sens Actuat B Chem 166\u2013167:320\u2013329. https:\/\/doi.org\/10.1016\/j.snb.2012.01.074","DOI":"10.1016\/j.snb.2012.01.074"},{"key":"1421_CR29","unstructured":"Wellenzohn K, B\u00f6hlen MH, Dign\u00f6s A, Gamper J, Mitterer H (2017) Continuous imputation of missing values in streams of pattern-determining time series. In: Proceedings of the 20th international conference on extending database technology, EDBT 2017, Venice, Italy, March 21\u201324, 2017, pp 330\u2013341"},{"key":"1421_CR30","unstructured":"Yi X, Zheng Y, Zhang J, Li T (2016) ST-MVL: filling missing values in geo-sensory time series data. In: Proceedings of the twenty-fifth international joint conference on artificial intelligence, IJCAI 2016, New York, NY, USA, 9\u201315 July 2016, pp 2704\u20132710"},{"issue":"5","key":"1421_CR31","doi-asserted-by":"publisher","first-page":"1477","DOI":"10.1109\/TBME.2018.2874712","volume":"66","author":"J Yoon","year":"2019","unstructured":"Yoon J, Zame WR, van der Schaar M (2019) Estimating missing data in temporal data streams using multi-directional recurrent neural networks. IEEE Trans Biomed Eng 66(5):1477\u20131490. https:\/\/doi.org\/10.1109\/TBME.2018.2874712","journal-title":"IEEE Trans Biomed Eng"},{"key":"1421_CR32","unstructured":"Yu H, Rao N, Dhillon IS (2016) Temporal regularized matrix factorization for high-dimensional time series prediction. In: Advances in neural information processing systems 29: annual conference on neural information processing systems 2016, December 5\u201310, 2016, Barcelona, Spain, pp 847\u2013855"},{"issue":"11","key":"1421_CR33","doi-asserted-by":"publisher","first-page":"2541","DOI":"10.1016\/j.jss.2012.05.073","volume":"85","author":"S Zhang","year":"2012","unstructured":"Zhang S (2012) Nearest neighbor selection for iteratively KNN imputation. J Syst Softw 85(11):2541\u20132552. https:\/\/doi.org\/10.1016\/j.jss.2012.05.073","journal-title":"J Syst Softw"},{"key":"1421_CR34","doi-asserted-by":"publisher","first-page":"933","DOI":"10.4236\/ojs.2014.411088","volume":"4","author":"X Zhu","year":"2014","unstructured":"Zhu X (2014) Comparison of four methods for handing missing data in longitudinal data analysis through a simulation study. Open J Stat 4:933\u2013944. https:\/\/doi.org\/10.4236\/ojs.2014.411088","journal-title":"Open J Stat"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-019-01421-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10115-019-01421-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-019-01421-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,14]],"date-time":"2020-11-14T00:27:13Z","timestamp":1605313633000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10115-019-01421-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,15]]},"references-count":34,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2020,6]]}},"alternative-id":["1421"],"URL":"https:\/\/doi.org\/10.1007\/s10115-019-01421-7","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,15]]},"assertion":[{"value":"26 November 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 October 2019","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 October 2019","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 November 2019","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}