Time series(TS) is one of the most common types of data, specifically, TS analyses play an important role in medical research. Unfortunately, time series in the medical domain tend to be missing and sparse, often leading to wrong results. While most algorithms take this into account, specific types of missing mechanisms, which are very common in medical data, are more difficult to attend to.
In this project, we aim to study the influence of missing data on the learning results and suggest better algorithms for learning time series in medical domains.
Can lead to a paper, depending on the results.