Attention-Based Multi-modal Missing Value Imputation for Time Series Data with High Missing Rate

Multivariate time series data is prone to a high missing rate which presents an obstacle to statistical analysis of the data. Imputation has become the standard measure to handle this challenge. However, existing time series missing value imputation methods are mostly uni-modal that relies on self-imputation. With an unprecedented rate of data collection, the availability of multi-modal data is increasing, allowing us the opportunity to impute the time series missing values using other datasets generated from the same cohort. In this paper, we propose a multi-modal time series missing value imputation framework, TSEst, that can utilize multiple data modalities to overcome the limitations of self-imputation. The framework uses additional cross-sectional or time series data for the imputation and therefore, is less affected by a high missing rate in the time series data. A comprehensive set of experiments on two datasets shows an improvement in imputation accuracy over the baselines. Experimental results also demonstrate that the improvement is caused by the effective integration of the additional data modality. The proposed framework can impute missing values in the samples with no time series data available, reducing the reliance on long-term data collection. Availability: Code is available at https://github.com/compbiolabucf/TSEst