Comments (1)
Thank you! I am excited to hear that it works for your data!
If I understand it correctly, the problem is that some entries in the data table you show are missing, and the positions of the missing entries could be arbitrary, and you want to predict them.
This is a very interesting problem. Unfortunately, DoppelGANger cannot handle this. In fact, we have been thinking of a similar problem (extending DoppelGANger for time series prediction like your case), which would require additional designs/approaches. I will update here if we get to that point.
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Related Issues (20)
- membership_inference_attack HOT 6
- CLI getting stuck on running example_training/main.py HOT 2
- Dynamic attributes / attributes with time stamp? HOT 6
- Request for min/max used for feature and attribute normalization in input data HOT 2
- The data generated ranges from 0 to 2 HOT 3
- Incomplete training HOT 7
- unreasonable output HOT 6
- Dataset HOT 1
- About two MLPs HOT 1
- Training does not run although the input is of the required form HOT 6
- Generating time series with negative values HOT 4
- is_gen_flag HOT 4
- Attribute problematic result HOT 18
- Problem with tensorflow HOT 1
- Training time HOT 6
- Code of AR and HMM baseline
- unknown output type HOT 6
- Request for availability of the scripts used to reproduce figures HOT 8
- Inference from attributes HOT 3
- Unable to run main.py in example_training HOT 21
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