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dc.contributor.authorDergachev, Valentin A.
dc.contributor.authorGorban, A. N.
dc.contributor.authorRossiev, A. A.
dc.contributor.authorKarimova, L. M.
dc.contributor.authorKuandykov, E. V.
dc.contributor.authorMakarenko, G. G.
dc.contributor.authorSteier, Peter
dc.date.accessioned2021-02-11T21:33:16Z
dc.date.available2021-02-11T21:33:16Z
dc.date.issued2001-01-01
dc.identifier.citationDergachev, V. A., Gorban, A. N., Rossiev, A. A., Karimova, L. M., Kuandykov, E. B., Makarenko, N. G., & Steier, P. (2001). The filling of gaps in geophysical time series by artificial neural networks. Radiocarbon, 43(2A), 365-371.
dc.identifier.issn0033-8222
dc.identifier.doi10.1017/S0033822200038224
dc.identifier.urihttp://hdl.handle.net/10150/654662
dc.descriptionFrom the 17th International Radiocarbon Conference held in Jerusalem, Israel, June 18-23, 2000.
dc.description.abstractNowadays, there is a large number of time series of natural data to study geophysical and astrophysical phenomena and their characteristics. However, short length and data gaps pose a substantial problem for obtaining results on properties of the underlying physical phenomena with existing algorithms. Using only an equidistant subset of the data with coarse steps leads to loss of information. We present a method to recover missing data in time series. The approach is based on modeling the time series with manifolds of small dimension, and it is implemented with the help of neural networks. We applied this approach to real data on cosmogenic isotopes, demonstrating that it could successfully repair gaps where data was purposely left out. Multi-fractal analysis was applied to a true radiocarbon time series after recovering missing data.
dc.language.isoen
dc.publisherDepartment of Geosciences, The University of Arizona
dc.relation.urlhttp://radiocarbon.webhost.uits.arizona.edu/
dc.rightsCopyright © by the Arizona Board of Regents on behalf of the University of Arizona. All rights reserved.
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectneural networks
dc.subjectself similarity
dc.subjecttime series analysis
dc.subjectcosmogenic elements
dc.subjectcalibration
dc.subjectmathematical methods
dc.subjectmathematical models
dc.subjectstatistical analysis
dc.subjectaccuracy
dc.subjectdata processing
dc.subjectC 14
dc.subjectcarbon
dc.subjectisotopes
dc.subjectradioactive isotopes
dc.subjectabsolute age
dc.titleThe Filling of Gaps in Geophysical Time Series by Artificial Neural Networks
dc.typeProceedings
dc.typetext
dc.identifier.journalRadiocarbon
dc.description.collectioninformationThe Radiocarbon archives are made available by Radiocarbon and the University of Arizona Libraries. Contact lbry-journals@email.arizona.edu for further information.
dc.eprint.versionFinal published version
dc.description.admin-noteMigrated from OJS platform February 2021
dc.source.volume43
dc.source.issue2A
dc.source.beginpage365
dc.source.endpage371
refterms.dateFOA2021-02-11T21:33:16Z


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