It feels to me as if you’re trying to solve a research problem, but asking us what the solution is, as if a perfect answer already existed in the back of the book.
Nobody knows for sure. Some approaches are better in certain situations, other approaches are better in other situations.
In general, you want to create a model with some stages. The first stage might be “shape recognition,” the next stage may be “change estimation,” and the last stage would be “scenario inferrence.”
Then you need to build the appropriate parameters of each of these models, and create the appropriate training sets, and then you can train them.
I’d probably start with something like a CNN for stage 1, some classical regression for stage 2, and a RNN for stage 3. But nothing says this is the only, or best, or even a workable solution. Only real trials will tell.
Btw, 90% of the hard work in machine learning is coming up with the appropriate parameterization of your problem, and creating enough training data based on that parameterization that you can actually build and test a high quality model. Despite the model itself getting all the headlines, that’s not the part that needs the most elbow grease.