Extending DTW 1-NN Classification to On-line Scenario I am familiar with Dynamic Time Warping classification using a 1-nearest neighbour approach. However, in most benchmark datasets and applications, it used ex-post, i.e. classifying a time series after you have observed it.

Is there a way to use the dynamic time warping similarity distance in an on-line setting before I have fully observed the series I am comparing?

Furthermore, if there is a way to probabilistically categorize as the series comes in that is even better. E.g. After observing 1/10 of the time series, the model predicts this sub-series has a 20% of being in group A, 10% of being in group B, etc
Hmm
Yes and no.
This is called "early classification". There as about two dozen papers that do this, but at least 20 are flawed...

See https://www.youtube.com/watch?v=d_qLzMMuVQg
http://www.cs.ucr.edu/~eamonn/UCRsuite.html