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Interpolation in Time Series: An Introductive Overview of Existing Methods, Their Performance Criteria and Uncertainty Assessment

Water Management Department, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1 (Building 23), 2628 CN Delft, The Netherlands
DEEP, INSA Lyon, Université de Lyon, 34 avenue des arts, F-69621 Villeurbanne CEDEX, France
Department of Hydraulic Engineering, Deltares, P.O. Box 177, 2600 MH Delft, The Netherlands
Author to whom correspondence should be addressed.
Water 2017, 9(10), 796;
Received: 31 July 2017 / Revised: 3 October 2017 / Accepted: 13 October 2017 / Published: 17 October 2017
(This article belongs to the Special Issue Quantifying Uncertainty in Integrated Catchment Studies)
PDF [547 KB, uploaded 20 October 2017]


A thorough review has been performed on interpolation methods to fill gaps in time-series, efficiency criteria, and uncertainty quantifications. On one hand, there are numerous available methods: interpolation, regression, autoregressive, machine learning methods, etc. On the other hand, there are many methods and criteria to estimate efficiencies of these methods, but uncertainties on the interpolated values are rarely calculated. Furthermore, while they are estimated according to standard methods, the prediction uncertainty is not taken into account: a discussion is thus presented on the uncertainty estimation of interpolated/extrapolated data. Finally, some suggestions for further research and a new method are proposed. View Full-Text
Keywords: comparison; criteria; interpolation; methods; uncertainty; review comparison; criteria; interpolation; methods; uncertainty; review

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Lepot, M.; Aubin, J.-B.; Clemens, F.H. Interpolation in Time Series: An Introductive Overview of Existing Methods, Their Performance Criteria and Uncertainty Assessment. Water 2017, 9, 796.

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