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.
@article{arxiv.1802.05910,
title = {Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space},
author = {Steven Van Vaerenbergh and Ignacio Santamaria and Victor Elvira and Matteo Salvatori},
journal= {arXiv preprint arXiv:1802.05910},
year = {2018}
}