  Effective imputation is a crucial preprocessing step for time series
analysis. Despite the development of numerous deep learning algorithms for time
series imputation, the community lacks standardized and comprehensive benchmark
platforms to effectively evaluate imputation performance across different
settings. Moreover, although many deep learning forecasting algorithms have
demonstrated excellent performance, whether their modeling achievements can be
transferred to time series imputation tasks remains unexplored. To bridge these
gaps, we develop TSI-Bench, the first (to our knowledge) comprehensive
benchmark suite for time series imputation utilizing deep learning techniques.
The TSI-Bench pipeline standardizes experimental settings to enable fair
evaluation of imputation algorithms and identification of meaningful insights
into the influence of domain-appropriate missingness ratios and patterns on
model performance. Furthermore, TSI-Bench innovatively provides a systematic
paradigm to tailor time series forecasting algorithms for imputation purposes.
Our extensive study across 34,804 experiments, 28 algorithms, and 8 datasets
with diverse missingness scenarios demonstrates TSI-Bench's effectiveness in
diverse downstream tasks and potential to unlock future directions in time
series imputation research and analysis. The source code and experiment logs
are available at https://github.com/WenjieDu/AwesomeImputation.
