What model do I use to predict a regression problem with timeseries data
[Updated]

If you have measured Reagent B for same number of time steps for each experiment, then:


Using these time steps as feature values, you can build a regression model using, Multi Layer Perceptions, XGBoost/LGBM/RandomForests etc.
Even Recurrent Neural Nets could work if enough data is available. Treat your time step values of Reagent B as single dimention embedding values and use BiLSTM architecture with Linear activation function in the last layer. Loss could be mean squared error. BiLSTM could work in your case as it makes sense to read from last time step to first time step also


If reagent B is measured for different time steps, in different experiments:


You can still use Ensemble Trees/MLP/RNN based methods, but you will have to consider adding paddings(imputing) or limiting max time steps to make number of time steps uniform across all experiments.


XGB/LGBM can take missing values as inputs and handle it well, so you dont need to do any imputation.