Multivariate Time Series Imputation: A Survey on available Methods with a Focus on hybrid GANs

Abstract

<p>Multivariate time series (MTS) are captured in a great variety of real-world applications. However, analysing and modelling the data for classification and forecasting purposes can become very challenging if values are missing in the data set. The need for imputation methods, to fill the gaps in MTS, is well known. Thus, a great variaty of algorithms for solving this task has been proposed in the literature. However, research community is constantly working on the development of advanced algorithms, that fulfill the special requirements of multidimensional temporal data, since most of the existing imputation methods treat MTS as ordinary structured data and fail to model the temporal relationships within and between sequences of observations. The main emphasis of MTS imputation research is currently put on deep learning (DL) models, especially models making use of generative adversarial networks (GANs). In our survey, we present a general categorization of imputation algorithms and introduce groups of hybrid GAN-models used for the MTS imputation task, which we investigate and discuss in detail. A quantitative comparison of the hybrid GANs’ performance regarding MTS imputation is presented based on our findings in the literature. </p>