Missing data simulation inside flow rate time-series using multiple-point statistics

Missing data simulation inside flow rate time-series using multiple-point statistics

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 da...

Missing data imputation in multivariable time series data

Multivariate time series data are found in a variety of fields such as bioinformatics, biology, genetics, astronomy, geography and finance. Many time series datasets contain missing data. Multivariate time series missing data imputation is a challenging topic and needs to be carefully considered before learning or predicting time series. Frequent researches have been done on the use of diffe...

Simulation of braided river elevation model time series with multiple-point statistics

3D multiple-point statistics simulation using 2D training images

One of the main issues in the application of multiple-point statistics (MPS) to the simulation of threedimensional (3D) blocks is the lack of a suitable 3D training image. In this work, we compare three methods of overcoming this issue using information coming from bidimensional (2D) training images. One approach is based on the aggregation of probabilities. The other approaches are novel. One ...

Reconstruction of Missing Data in Synthetic Time Series Using EMD

The paper presents a novel method for reconstruction of missing data in time series. The method is based on the decomposition of known parts of time series into monocomponents (Intrinsic Mode Functions, IMF) using Empirical Mode Decomposition (EMD), construction of prediction models for each IMF using known parts of times series and their composition using weighted average. We demonstrate the e...