  Researchers have been persistently working to address the issue of missing
values in time series data. Numerous models have been proposed, striving to
estimate the distribution of the data. The Radial Basis Functions Neural
Network (RBFNN) has recently exhibited exceptional performance in estimating
data distribution. In this paper, we propose a time series imputation model
based on RBFNN. Our imputation model learns local information from timestamps
to create a continuous function. Additionally, we incorporate time gaps to
facilitate learning information considering the missing terms of missing
values. We name this model the Missing Imputation Multivariate RBFNN
(MIM-RBFNN). However, MIM-RBFNN relies on a local information-based learning
approach, which presents difficulties in utilizing temporal information.
Therefore, we propose an extension called the Missing Value Imputation
Recurrent Neural Network with Continuous Function (MIRNN-CF) using the
continuous function generated by MIM-RBFNN. We evaluate the performance using
two real-world datasets with non-random missing and random missing patterns,
and conduct an ablation study comparing MIM-RBFNN and MIRNN-CF.
