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Imputing missing data from a multivariate time series dataset remains a challenging problem. There is an abundance of research on using various techniques to impute missing, biased, or corrupted values to a dataset. While a great amount of work has been done in this field, most imputing methodologies are centered about a specific application, typically involving static data analysis and simple time...
This part of the paper introduces a comparison of VAR-IM algorithm with modern techniques used for missing data analysis. Quantitative methods are usually developed based on some fundamental understanding of the statistical analysis of the missing data. Various types of quantitative methods such as K nearest neighbour (KNN), (Multivariate Autoregressive state-Space) MARRS package and EM algorithm...
Given an observed data set, there are different methods that can be used to impute missing data. While excellent work has been done in this field, most available approaches are focused on some particular applications, such as static data and univariate time series. The primary aim of the two papers Part I VAR-IM algorithm v.s. traditional methods and Part II VAR-IM algorithm v.s. modern algorithms...
Most missing data analysis techniques have focused on using model parameter estimation which depends on modern statistical data analysis methods such as maximum likelihood and multiple imputation. In fact, these modern methods are better than traditional methods (for example, complete data analysis and mean imputation approaches), and in many particular applications can give unbiased parametric estimation...
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