Missing values are really common in data science. However, learning a model is really difficult for time series with missing data. In this video, we are using DLM, which builds a model from single components of a time series, to analyze PM 2.5 series. DLM can be used for learning time series with missing data. Furthermore, DLM would also generate one-step ahead prediction. That is what we are going to use for imputation.
The link to the video is here
A little background story: I got the idea of making this video after completing a course called AI and the Public Health from DeepLearning.AI. The use case in that course is also about PM 2.5 in Bogotá, so I thought of a different approach to it.