Comments (1)
Hi there,
Yes, this is listed as a future feature. Adding the requisite recurrent cells significantly complicates optimisation, and so for now we've avoided doing so. LSTM is the obvious choice, but GRUs seem more stable. The problem is structuring the data in such a way that training can be done efficiently without constant IO between CPU and GPU. This is trivial if you're building an LSTM net for your own dataset, but far harder for me writing code to work on any dataset.
For the moment, it is usually enough to pass manually lagged data into the additional_data argument, and MIDAS can generally learn the relationship to past and future variables. This is kind of like manually adding a polynomial of time.
As for multilevel data, again, there is no explicit handling of this at this time. As far as I'm aware, there is very little work done on multilevel modelling with neural networks. I'd recommend concatenating the relevant hierarchical variables/passing into additional_data. This ought to work a little better in future when I add feature embeddings to categorical variables.
Regards,
Alex
from midas.
Related Issues (12)
- In regards to the background of the model HOT 5
- Issue in running demo HOT 3
- Compatibility with Python 2.7 HOT 2
- Impute unseen/test data HOT 14
- GPU utilization in AWS HOT 6
- Demo crashes kernel HOT 1
- Research paper HOT 1
- Do you have an input pipeline example HOT 3
- The file formed in the MIDAS, where it get save .Is the method mentioned can be used without introdusing noise. HOT 1
- Categorical variables and multiple imputation HOT 1
- incompatible with tensorflow 2.0 HOT 1
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from midas.