  Time series data are observations collected over time intervals. Successful
analysis of time series data captures patterns such as trends, cyclicity and
irregularity, which are crucial for decision making in research, business, and
governance. However, missing values in time series data occur often and present
obstacles to successful analysis, thus they need to be filled with alternative
values, a process called imputation. Although various approaches have been
attempted for robust imputation of time series data, even the most advanced
methods still face challenges including limited scalability, poor capacity to
handle heterogeneous data types and inflexibility due to requiring strong
assumptions of data missing mechanisms. Moreover, the imputation accuracy of
these methods still has room for improvement. In this study, I developed
tsDataWig (time-series DataWig) by modifying DataWig, a neural network-based
method that possesses the capacity to process large datasets and heterogeneous
data types but was designed for non-time series data imputation. Unlike the
original DataWig, tsDataWig can directly handle values of time variables and
impute missing values in complex time series datasets. Using one simulated and
three different complex real-world time series datasets, I demonstrated that
tsDataWig outperforms the original DataWig and the current state-of-the-art
methods for time series data imputation and potentially has broad application
due to not requiring strong assumptions of data missing mechanisms. This study
provides a valuable solution for robustly imputing missing values in
challenging time series datasets, which often contain millions of samples, high
dimensional variables, and heterogeneous data types.
