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arXiv · 1802.05910

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

Steven Van Vaerenbergh·Ignacio Santamaria·Victor Elvira·Matteo Salvatori
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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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Steven Van Vaerenbergh, Ignacio Santamaria, Victor Elvira, Matteo Salvatori. 2018-02-16. Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space. https://arxiv.org/abs/1802.05910

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