While hierarchical generative modules offer several advantages in capturing complex temporal dependencies and non-stationarity within multivariate time series data, there are also potential limitations associated with their use. One drawback is increased computational complexity due to the hierarchical structure, which may lead to longer training times and higher resource requirements. Another limitation is related to interpretability, as hierarchically generated representations may be more challenging to explain or understand compared to simpler models. Moreover, designing an effective hierarchy that captures all relevant features at different scales without introducing noise or redundancy can be a challenging task.