Scaling unknown time series for prediction with RNN

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?

• It's not clear what you mean by "scale (normalize)" the ABP series. Are you just scaling to the maximum and minimum value of each individual series? It seems strange that you are having trouble with ABP values in particular, unless they aren't already expressed in standard units like mm Hg. – EdM May 10 '18 at 20:03