Handling Missing Data in Multivariate Time Series Using a Vector Autoregressive Model Based Imputation (VAR-IM) Algorithm. Part II: VAR-IM Algorithm Versus Modern Methods

摘要

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 are discussed. The relative advantages and disadvantages of these approaches are highlighted. The performance of the vector autoregressive model based imputation methods is compared with that of three existing methods, namely, KNN, MARRRS and EM for dealing with missing data in multivariate time series, where an ECG dataset is used as an a case study. The results show that VAR-IM produces a better recovering performance for missing values than the other three methods. The advantages and limitations of the VAR-IM algorithm is also discussed.