Pattern localization in time series through signal-to-model alignment in latent space
Ver/ Abrir
Registro completo
Mostrar el registro completo DCAutoría
Van Vaerenbergh, Steven; Santamaría Caballero, Luis Ignacio; Elvira Arregui, Víctor; Salvatori, MatteoFecha
2018Derechos
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publicado en
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, Canada, 2018, 2711-2715
Editorial
IEEE
Enlace a la publicación
Palabras clave
Pattern localization
Dynamic time warping
Canonical correlation analysis
Time series
Alignment
Resumen/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.
Colecciones a las que pertenece
- D12 Congresos [564]
- D12 Proyectos de Investigación [459]