Pattern localization in time series through signal-to-model alignment in latent space
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AuthorVan Vaerenbergh, Steven; Santamaría Caballero, Luis Ignacio; Elvira Arregui, Víctor; Salvatori, Matteo
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 nondestructive testing demonstrate that the proposed approach shows significant improvements over the state of the art.