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augusts99 OP t1_ja5y2rt wrote

Reply to comment by thehallmarkcard in Implementation of RNN as post-processing by augusts99

Yeah! Currently what I do is that Model 1 makes predictions based on certain input features making predictions timestep for timestep. The LSTM model uses the predicted sequence together with other variable sequences to make the predictions more robust and stable, as well as more making it have more correct trends. Atleast, that is the idea.

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thehallmarkcard t1_ja604kn wrote

So with no other info on your methodology I can’t think of any issue with this. In some sense you’re RNN may be modeling the trend component and the other model measuring the volatility. But that’s hard to say not knowing any more. I am curious if you tried stacking the models directly such that the weights optimize through both models simultaneously. But that depends what kind of models you have and isn’t necessarily better just different.

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augusts99 OP t1_ja7jo91 wrote

Okay thank you for the feedback! That could be interesting! Model 1 is a Random Forest model and uses different input than the LSTM, and at the moment I think for my skill level it may be a too big of a hassle to make the models predict simultaneously. Or what is meant with stacking the models if I may ask?

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FunBit9789 t1_ja8aim7 wrote

Ah ok so if it were also a NN you could have outputs feed directly from model to the other with multiple heads but with a random forest as the first model my suggestion doesn’t really make sense.

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