Overall Goal To predict how much reagent "A" I started with in a reaction.
Data: To predict this I have timeseries data of reagent "B". For each time step a measurement of reagent "B" is taken (the amount of reagent "B" present at that time point). The overall timeseries is a curve. This curve may change based on how much reagent "A" I start with.
My question is what model should I use to predict reagent A? Will a Recurrent NN work? I have only seen RNN used in predicting the very next time steps or to classify something based on the timeseries. I am looking for the model to use time series data to predict a regression problem.