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Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space

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 non-destructive testing demonstrate that the proposed approach shows significant improvements over the state of the art.

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Paper Details

Authors:
Steven Van Vaerenbergh, Ignacio Santamaría, Víctor Elvira, Matteo Salvatori
Submitted On:
12 April 2018 - 11:50am
Short Link:
Type:
Poster
Event:
Presenter's Name:
Ignacio Santamaria
Paper Code:
3652
Document Year:
2018
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Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space

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[1] Steven Van Vaerenbergh, Ignacio Santamaría, Víctor Elvira, Matteo Salvatori, "Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2412. Accessed: Oct. 23, 2018.
@article{2412-18,
url = {http://sigport.org/2412},
author = {Steven Van Vaerenbergh; Ignacio Santamaría; Víctor Elvira; Matteo Salvatori },
publisher = {IEEE SigPort},
title = {Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space},
year = {2018} }
TY - EJOUR
T1 - Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space
AU - Steven Van Vaerenbergh; Ignacio Santamaría; Víctor Elvira; Matteo Salvatori
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2412
ER -
Steven Van Vaerenbergh, Ignacio Santamaría, Víctor Elvira, Matteo Salvatori. (2018). Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space. IEEE SigPort. http://sigport.org/2412
Steven Van Vaerenbergh, Ignacio Santamaría, Víctor Elvira, Matteo Salvatori, 2018. Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space. Available at: http://sigport.org/2412.
Steven Van Vaerenbergh, Ignacio Santamaría, Víctor Elvira, Matteo Salvatori. (2018). "Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space." Web.
1. Steven Van Vaerenbergh, Ignacio Santamaría, Víctor Elvira, Matteo Salvatori. Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2412