Is there a formulation of DTW that allows to deal with uncertainty or to utilize knowledge of the probabilistic structure of the signal? I would like to have a formulation similar to HMM but stay in DTW paradigm.
Why do I need that? I have several examples of a time series pattern that I want to find in the data. The amount of examples is not enough to build a complex model (e.g. HMM). On the other hand, I would like to leverage all the examples given at once and not use these examples one after another. I am wondering if there is DTW formulation that allows to account for that.