  In this paper, we study the problem of locating a predefined sequence of
patterns in a time series. In particular, the studied scenario assumes a
theoretical model is available that contains the expected locations of the
patterns. This problem is found in several contexts, and it is commonly solved
by first synthesizing a time series from the model, and then aligning it to the
true time series through dynamic time warping. We propose a technique that
increases the similarity of both time series before aligning them, by mapping
them into a latent correlation space. The mapping is learned from the data
through a machine-learning setup. Experiments on data from non-destructive
testing demonstrate that the proposed approach shows significant improvements
over the state of the art.
