Multi-step prediction of a dynamic time series with jumps and decay curves using NARX networks

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Torsten K
Torsten K 2020-9-1
Hello everybody,
I am trying to predict a dynamic time series with a sequence of sloping curve sections with the help of a NARX network. The aim is to predict the further course of the curve segments for 60 minutes each. The following shows three diagrams with the target variable T and the 3 input variables X1 to X3 for 25,000 minutes:
X1 represents the time after the peak, while variables X2 and X3 are measured process variables. The length of the entire time series is 150,000 minutes.
My basic approach is this:
1) Splitting the time series into a training data set (TD, 120,000 minutes) and a prediction data set (PD, 30,000 minutes).
2) Using divideind to split TD into training, validation and test data sets (84000/18000/18000 minutes)
3) Open-loop training with TD with variation of the hyperparameters
4) Closed-loop re-training with TD
5) Test the MSP with PD.
Does anyone have any suggestion how best to solve this problem?
Kind regards
Torsten

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