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 modelling 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 missing rates 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. All source code and experiment logs are released at https://github.com/WenjieDu/AwesomeImputation.

翻译：有效的插补是时间序列分析中至关重要的预处理步骤。尽管已开发出众多用于时间序列插补的深度学习算法，但该领域仍缺乏标准化且全面的基准平台，以有效评估不同设置下的插补性能。此外，尽管许多深度学习预测算法已展现出卓越性能，但其建模成果能否迁移至时间序列插补任务仍有待探索。为弥补这些不足，我们开发了TSI-Bench，这是首个（据我们所知）利用深度学习技术构建的综合性时间序列插补基准套件。TSI-Bench流程标准化了实验设置，以实现对插补算法的公平评估，并深入探究领域相关的缺失率与缺失模式对模型性能的影响。此外，TSI-Bench创新性地提供了一种系统化范式，可将时间序列预测算法定制用于插补任务。我们在34,804次实验、28种算法和8个具有多样化缺失场景的数据集上进行的广泛研究，证明了TSI-Bench在多种下游任务中的有效性，及其在开辟时间序列插补研究与分析未来方向方面的潜力。所有源代码与实验日志已发布于https://github.com/WenjieDu/AwesomeImputation。