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One can create a time series model to predict a target variable. What I need to do is find the input combinations and sequences that impact the target variable the most. In this case, the input data is a series of time steps, each of which has many features. The desired model would give clarity to not only the best features but the combinations and sequences of combinations that impact the target the most. This is desired because it models a system that I'd like to optimize based on this analysis.

Hope that makes sense. Ideas?

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    $\begingroup$ It may be useful to provide a minimal example of the structure of your dataset here. $\endgroup$ Sep 25, 2020 at 12:22

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With machine learning, I could think that process analytics is what you are looking for:

I imagine you may have a dataset like this:

[step1, step2, step3]  -> [No Target]
[step1, step2]         -> [Target]
...

Business Process Analytics is the family of methods and tools that can be applied to these event streams in order to support decision-making in organizations. The analysis of process events can focus on the behavior of completed processes, evaluate currently running process instances, or focus on predicting the behavior of process instances in the future. Process Analytics

Is the most similar process (to your question) in Analytics I know

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