I used the forecast package to forecast the daily time-series of variable Y using its lag values and a time series of an external parameter X. I found nnetar model (a NARX model) was the best in terms of overall performance. However, I was not able to get the prediction of peaks of the time series well despite my various attempts with parameter tuning.
I then extracted the peak values (above a threshold) of Y (and of course this is not a regular time series anymore) and corresponding X values and tried to fit a regression model (note: not an autoregression model) using various models in carat package. I found out the prediction of peak values using brnn(Bidirectional recurrent neural networks) model just using X values is better than that of nnetar which uses both lag values and X values.
Now my question is how do I go from here to create ensamples of these two models (i.e whenever the prediction using brnn regression model ( or any other regression model) is better I want to replace the prediction using nnetar and move forward - I am mostly concerned about the peaks)? Is this a commonly used approach?
Instead of trying to pick one model that would be the superior at anytime, it's typically better to do an average of the models, in order to include as many individual views as possible. In the experiments I've been involved in, where we tried to pick one model that would outperform, based on historical performance, it's typically shown that a simple average was as good or better. Which is in line with the typical results on this problem: https://otexts.com/fpp2/combinations.html
So, before you try to go more advanced at it by using trying to pick a specific model based on previous performance, or by using an weighted average, consider doing a simple average of the two models.
If you want to continue with a sort of selection/weighted averaging, try to have a look at the FFORMA package in R: https://github.com/pmontman/fforma I've not tried the specific package (yet), but have seen promising results in my test using the original m4metalearning package.