Why do models trained on discretised continuous data outperform their continuous contrrparts

Hey, From experience, I've discovered that when training a neural network on discretised continuous data (Say, an RNN on time series data), it usually outperforms the continuous model in generalisation and accuracy.

I was wondering why this is the case, It feels like the answer would be something really simple and I'm totally missing it but it's been bugging me for some time. The obvious thing is the objective function changing from the MSE to Log likelihood.

Anyone care to …

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