## 4 Experiment
### Result
#### 4.4.1 Compare VS-GRU with Baselines  
It is apparent that forward imputation outperforms mean imputation and zero imputation in all models, which suggests that maintaining the time series feature in itself is important. Because zero imputation can carry information from missing patterns like mask indicators, zero imputation beats mean imputation in many cases. With the mask indicator, RNN models can achieve better performances except that LSTM shows almost the same performances in the ICD-9 Code tasks on MIMIC-III. On the other hand, sometimes nonRNN models fail with the mask indicator. We can see that the missing pattern is beneficial for helping the RNN model to get better results. On the other hand, results on the two datasets show that applying simple feature engineering on the time series can lead to better performances. If we remove the missing rate \(\mu\) from the input, all experimental results decrease except logistic regression in dealing with the multi-task problem on PhysioNet, which is almost the same. It is safe to say that the missing rate \(\mu\) plays an effective role in the problem of multivariate time series with massive missing values. Next, we will discuss the experimental results of four versions of our model. Except for all four tasks problems in PhysioNet, all four versions of our model rank within the top five. In the single-label classification problem of the two datasets, there is a significant improvement between VS-GRU and the other baselines. Interestingly, with or without a dynamic imputation mechanism, VS-GRU-i does not improve the performance of the model. Because VS-GRU-i processes multivariate time series separately at first and then applies a second GRU layer to integrate all variables, it should strike a balance between these two procedures. However, it fails when dealing with such a simple problem. We indicate that a simple framework such as VS-GRU is effective enough to solve the single-label classification problem. A fully connected layer can integrate the variable well. Comparing the results between VS-GRU and VS-GRU w/o di in two datasets, the dynamic imputation mechanism is effective in PhysioNet but fails in MIMIC-III. Because the average missing rate is 0.9559 in MIMIC-III, it is very common that time series in MIMIC-III has completely missing variables. When dealing with completely missing variables, a dynamic imputation method is equal to mean imputation. When we replace dynamic imputation method with forward imputation, it is equal to zero imputation when dealing with completely missing variables. As the experimental results shown from Tables 1 and 2, zero imputation outperforms mean imputation. Thus, we should notice the limit of dynamic imputation mechanism and try to use it carefully. On the other hand, situations are quite the opposite in multi-label classification problems. VS-GRU-i performs the best in both datasets. The dynamic imputation mechanism fails in the multi-task classification problems. We suggest that using only a few parameters of each variable to impute the missing values cannot correctly map the multi-label into feature space. It may be effective when dealing with a single label, but it cannot capture all the information from multi-labels atthe same time. We indicate that it is also the same reason why VS-GRU fails in multi-task classification problems due to its lack of parameters.