Using a loop to predict longer vectors with a LSTM I have a model based on LSTMs that can predict a vector output based on a vector input. I can't increase the size of the output because :


I would need a larger network to obtain good results
It would take more time to train
The behaviour of my timeseries can be captured in the size of the vector I'm already predicting


So I tried to predict an output, and then use that prediction as a new input in my model and predict a new vector (without re-training). I iterate 5 times and I get this result :



As you can see, the first prediction is pretty good. Then, my model is lost and loses its accuracy. Do you know how to fix it ?

Thanks
I'm going to answer to myself since I managed to fix that issue. The problem is that my model was overfitting waaaay too much and I had to add a dropout between each layer.

Now the result is better :



I will now re-tune my model and improve it a bit.

note to myself : don't be lazy Julien, plot the validation loss next time

EDIT :

I trained it during 36 mn with more neurons per layer. Without overfitting, it's way way better.



Now I can predict 8 days of datapoints with a better accuracy.