TY - JOUR
T1 - Interpolation in Time Series
T2 - An Introductive Overview of Existing Methods, Their Performance Criteria and Uncertainty Assessment
AU - Lepot, Mathieu
AU - Aubin, Jean Baptiste
AU - Clemens, Francois
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - comparison
KW - review
KW - uncertainty
KW - methods
KW - interpolation
KW - criteria
KW - OA-Fund TU Delft
UR - http://resolver.tudelft.nl/uuid:a1be9972-78e5-4e18-8a26-a27a7582ee2a
U2 - 10.3390/w9100796
DO - 10.3390/w9100796
M3 - Article
SN - 2073-4441
VL - 9
JO - Water
JF - Water
IS - 10
M1 - 796
ER -