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