I'm trying to build a RNN model to predict arterial blood pressure (ABP) time series based on two other time series, namely, ECG and PPG.
It is available to me a set of multivariate time series of the form [ECG, PPG, ABP]. I use these multivariate time series to train an RNN model (inputs: [PPG, ECG], output [ABP]). The final objective of the model is to predict a [ABP] series that is not in the initial set ([PPG, ECG] series are to be collected in real time through sensors in order to a unknown [ABP] series to be predicted).
To train the RNN model successfully I have to scale (normalize) all the series available in the set (if I don't do this, i.e., don't scale the ABP series, the model outputs a constant).
The problem is that the different available ABP series have different scales, so I cant't simply use the scaling factors of these series to "inverse" scale the series obtained when the model is fed by the sensor's PPG and ECG signals.
Bottom line, I don't know how to "inverse" scale the output of my model and this is a fundamental task for the problem. So the questions are: how can I work this situation around? What is the best practice in this case?