LSTM with auxiliary data achieves abismal results I have a fairly simple LSTM models that achieves ok results.


I wanted to add auxiliary data as appeared in the keras docs. I added only one binary variable. I expected this model to be at least as good, assuming that if it has no information its weights will go to zero. In reality, the model fails and just predicts zeros for my entire test set.
The second model:

More specifically, I'm predicting a time series of human-behaviour, and my binary feature tags whether the time point is in a weekend. I'd expect it capture some of the weekend (black background) reduction, and if not, at least not to ruin the model completely.
Is there a reason why this new information has to be a part of your model? There's no rule that says that all data must be fed into your model. Maybe you have just found a feature that shouldn't be included in your predictive phases? And if so, that's perfectly fine, there is nothing wrong with that.