Missing data simulation inside flow rate time-series using multiple-point statistics. (December 2016) - Oriani, Fabio; Borghi, Andrea; Straubhaar, Julien; Mariethoz, Grégoire; Renard, Philippe
Abstract: The direct sampling (DS) multiple-point statistical technique is proposed as a non-parametric missing data simulator for hydrological flow rate time-series. The algorithm makes use of the patterns contained inside a training data set to reproduce the complexity of the missing data. The proposed setup is tested in the reconstruction of a flow rate time-series while considering several missing data scenarios, as well as a comparative test against a time-series model of type ARMAX. The results show that DS generates more realistic simulations than ARMAX, better recovering the statistical content of the missing data. The predictive power of both techniques is much increased when a correlated flow rate time-series is used, but DS can also use incomplete auxiliary time-series, with a comparable prediction power. This makes the technique a handy simulation tool for practitioners dealing with incomplete data sets. Highlights: A resampling technique is applied to missing flow rate data simulation. The proposed technique generates realistic temporal data patterns. Generally, the statistical content is entirely recovered even in large gaps. The setup can use an auxiliary time-series to condition the simulation. An incomplete auxiliary time-series can be used, with increased prediction power.