I need to forecast two non-correlated time-series (non-stationary). A sample is presented below:
414049364,21773560 414049656,21773926 414049938,21774287 414050204,21774638 414050453,21774975 414050682,21775296 414050895,21775597 414051093,21775874 414051278,21776125 414051453,21776344 414051620,21776530 414051780,21776678 414051935,21776785 414052089,21776849 414052242,21776865
The above is the input (two attributes) and the output (prediction) is composed of two targets (the same as input) for instance,
However, current regression techniques only consider a single attribute (class) forecasting but two or more. I've checked the following site https://machinelearningmastery.com/multi-output-regression-models-with-python/ for multi-target regression or predictive clustering trees. Unfortunately, I don't know how to adapt my data to those techniques. Ideally, I would like to predict multiple steps.