Time Series Imputation with Multivariate Radial Basis Function Neural Network
This work addresses the problem of missing data imputation for time series analysis, which is incremental as it builds on existing RBFNN methods with extensions for temporal handling.
The paper tackles missing value imputation in time series data by proposing MIM-RBFNN, a model based on Radial Basis Functions Neural Network that learns local information and time gaps, and extends it to MIRNN-CF to better utilize temporal information, achieving performance evaluated on real-world datasets with non-random and random missing patterns.
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.