Time series with missing data constitute an important setting for machine learning. The most successful prior approaches for modeling such time series are based on recurrent neural networks that learn to impute unobserved values and then treat the imputed values as observed. We start by introducing Recursive Input and State Estimation (RISE), a general framework that encompasses such prior approaches as specific instances. Since RISE instances tend to suffer from poor long-term performance as errors are amplified in feedback loops, we propose Direct Input and State Estimation (DISE), a novel framework in which input and state representations are learned from observed data only. The key to DISE is to include time information in representation learning, which enables the direct modeling of arbitrary future time steps by effectively skipping over missing values, rather than imputing them, thus overcoming the error amplification encountered by RISE methods. We benchmark instances of both frameworks on two forecasting tasks, observing that DISE achieves state-of-the-art performance on both.
The authors propose a new univariate time series analysis framework called RISE, which unifies the existing work on adapting RNNs to irregular time series. Building on top of the RISE, they propose a modification called DISE in which the algorithm skips the intervals without any observations. In that sense, DISE can be considered a marked point process analysis algorithm. They quantify the performance of the RISE and DISE on two datasets.
Table 1 is a valuable summary of the existing efforts on adapting RNNs to irregular time series. However, the paper overstates its scope. This work only studies RNNs. There are many alternatives for analysis of irregular time series, including Gaussian processes [1], ordinary differential equations [2], convolutional neural networks [3], neural point processes and spiking neural networks [4]. These references are only notable examples from each category and there are many more.
A major limitation of this paper is that it only applies to the univariate time series. Usually in domains such as healthcare, almost always different variables have different missing rates. Multiple works address the multivariate case, see [5] for example.
The main dataset used for evaluation is the Glucose dataset. However, this dataset is a peculiar and very specific dataset because its goal is to predict glucose level for type-1 diabetics only base on the past glucose measurements. While this task is meaningful, human biology states that forecasting glucose levels without knowledge of insulin injection or carbohydrate consumption is an extremely difficult task. In this setting, the most useful data is the latest data point. This dataset is an extreme forecasting task in absence of major predictors and I do not think it should be the primary dataset for evaluation of a new algorithm. 
Finally, the main idea of skipping the intervals without measurements is not very novel given the existing literature on neural point processes. Also, it is not enough contribution for a full conference paper.
[1] Shukla, Marlin (2019) Interpolation-Prediction Networks for Irregularly Sampled Time Series. In ICLR.
[2] Chen, T. Q., Rubanova, Y., Bettencourt, J., & Duvenaud, D. K. (2018). Neural ordinary differential equations. In NeurIPS.
[3] Nguyen, P., Tran, T., Wickramasinghe, N., & Venkatesh, S. (2016). Deepr: a convolutional net for medical records. IEEE BHI.
[4] Islam, K. T., Shelton, C. R., Casse, J. I., & Wetzel, R. (2017). Marked point process for severity of illness assessment. In Machine Learning for Healthcare Conference.
[5] Che, Z., Purushotham, S., Li, G., Jiang, B., & Liu, Y. (2018). Hierarchical deep generative models for multi-rate multivariate time series. In ICML.