@conference{10902/15197,
year = {2018},
url = {http://hdl.handle.net/10902/15197},
abstract = {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.},
organization = {The work of Steven Van Vaerenbergh was supported by the Ministerio de Economía, Industria y Competitividad (MINECO) of Spain under grant TEC2014-57402-JIN (PRISMA). The work of Víctor Elvira was supported
by the Agence Nationale de la Recherche of France under PISCES project (ANR-17-CE40-0031-01).},
publisher = {IEEE},
publisher = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, Canada, 2018, 2711-2715},
title = {Pattern localization in time series through signal-to-model alignment in latent space},
author = {Vaerenbergh, Steven van and Santamaría Caballero, Luis Ignacio and Elvira Arregui, Víctor and Salvatori, Matteo},
}