Missing Data Imputation and Meta-analysis on Correlation of Spatio-Temporal Weather Series Data

2021 ◽  
Author(s):  
Alkiviadis Kyrtsoglou ◽  
Dimara Asimina ◽  
Dimitrios Triantafyllidis ◽  
Stelios Krinidis ◽  
Konstantinos Kitsikoudis ◽  
...  
Keyword(s):  
Missing Data ◽  
Meta Analysis ◽  
Series Data ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Spatio Temporal

scholarly journals Missing data imputation of high‐resolution temporal climate time series data

2020 ◽  
Vol 27 (1) ◽  
Author(s):  
E Afrifa‐Yamoah ◽  
U. A. Mueller ◽  
S. M. Taylor ◽  
A. J. Fisher
Keyword(s):  
Time Series ◽  
Missing Data ◽  
High Resolution ◽  
Time Series Data ◽  
Series Data ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Climate Time Series

Missing data imputation for traffic flow speed using spatio-temporal cokriging

2018 ◽  
Vol 88 ◽  
pp. 124-139 ◽  
Author(s):  
Bumjoon Bae ◽  
Hyun Kim ◽  
Hyeonsup Lim ◽  
Yuandong Liu ◽  
Lee D. Han ◽  
...  
Keyword(s):  
Missing Data ◽  
Traffic Flow ◽  
Flow Speed ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Spatio Temporal

scholarly journals SparRec: An effective matrix completion framework of missing data imputation for GWAS

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Bo Jiang ◽  
Shiqian Ma ◽  
Jason Causey ◽  
Linbo Qiao ◽  
Matthew Price Hardin ◽  
...  
Keyword(s):  
Missing Data ◽  
Association Studies ◽  
Meta Analysis ◽  
Matrix Completion ◽  
Low Rank ◽  
Genome Wide Association Studies ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Low Rank Matrix Completion ◽  
Similar Accuracy

Abstract Genome-wide association studies present computational challenges for missing data imputation, while the advances of genotype technologies are generating datasets of large sample sizes with sample sets genotyped on multiple SNP chips. We present a new framework SparRec (Sparse Recovery) for imputation, with the following properties: (1) The optimization models of SparRec, based on low-rank and low number of co-clusters of matrices, are different from current statistics methods. While our low-rank matrix completion (LRMC) model is similar to Mendel-Impute, our matrix co-clustering factorization (MCCF) model is completely new. (2) SparRec, as other matrix completion methods, is flexible to be applied to missing data imputation for large meta-analysis with different cohorts genotyped on different sets of SNPs, even when there is no reference panel. This kind of meta-analysis is very challenging for current statistics based methods. (3) SparRec has consistent performance and achieves high recovery accuracy even when the missing data rate is as high as 90%. Compared with Mendel-Impute, our low-rank based method achieves similar accuracy and efficiency, while the co-clustering based method has advantages in running time. The testing results show that SparRec has significant advantages and competitive performance over other state-of-the-art existing statistics methods including Beagle and fastPhase.

scholarly journals Spatio-temporal missing data reconstruction in satellite displacement measurement time series

2020 ◽  
Author(s):  
Alexandre Hippert-Ferrer ◽  
Yajing Yan ◽  
Philippe Bolon
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Missing Values ◽  
Satellite Image ◽  
Displacement Measurement ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Spatial Coverage ◽  
Spatio Temporal ◽  
Offset Tracking

<p>Time series analysis constitutes a thriving subject in satellite image derived displacement measurement, especially since the launching of Sentinel satellites which provide free and systematic satellite image acquisitions with extended spatial coverage and reduced revisiting time. Large volumes of satellite images are available for monitoring numerous targets at the Earth’s surface, which allows for significant improvements of the displacement measurement precision by means of advanced multi-temporal methods. However, satellite image derived displacement time series can suffer from missing data, which is mainly due to technical limitations of the ground displacement computation methods (e.g. offset tracking) and surface property changes from one acquisition to another. Missing data can hinder the full exploitation of the displacement time series, which can potentially weaken both knowledge and interpretation of the physical phenomenon under observation. Therefore, an efficient missing data imputation approach seems of particular importance for data completeness. In this work, an iterative method, namely extended Expectation Maximization - Empirical Orthogonal Functions (EM-EOF) is proposed to retrieve missing values in satellite image derived displacement time series. The method uses both spatial and temporal correlations in the displacement time series for reconstruction. For this purpose, the spatio-temporal covariance of the time series is iteratively estimated and decomposed into different EOF modes by solving the eigenvalue problem in an EM-like scheme. To determine the optimal number of EOFs modes, two robust metrics, the cross validation error and a confidence index obtained from eigenvalue uncertainty, are defined. The former metric is also used as a convergence criterion of the iterative update of the missing values. Synthetic simulations are first performed in order to demonstrate the ability of missing data imputation of the extended EM-EOF method in cases of complex displacement, gaps and noise behaviors. Then, the method is applied to time series of offset tracking displacement measurement of Sentinel-2 images acquired between January 2017 and September 2019 over Fox Glacier in the Southern Alps of New Zealand. Promising results confirm the efficiency of the extended EM-EOF method in missing data imputation of satellite image derived displacement time series.</p>

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