I experimented recently with predicting histograms cutting the time series into slices and learning the histogram for each piece. Then using histogram(i) to histogram(i+1) predictions.
I wonder if the same could be done also with objects like machine learning networks. That is. Predicting the network parameters.
So the idea is to cut the time data into slices. Then I will try experimenting with setting the input as object(slice (i)) and the target as object(slice (i+1)). After training the model. I ?will be able to predict a network model object for a future object with the last object as input.
I probably need objects that does not have so sensitive parameters so it allows for a little error.