Continuous-time Autoencoders for Regular and Irregular Time Series Imputation

Time series imputation is one of the most fundamental tasks for time series. Real-world time series datasets are frequently incomplete (or irregular with missing observations), in which case imputation is strongly required.

时间序列补全（Time Series Imputation）是时间序列分析领域最为基础的任务之一。现实场景中的时间序列数据集常存在不完整的情况（或因观测值缺失而呈现非规则特征），此时亟需开展补全操作。