Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space
| Title | Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Van Vaerenbergh, S., I. Santamaría, V. Elvira, and M. Salvatori |
| Conference Name | 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
| Month Published | April |
| Publisher | IEEE |
| Conference Location | Calgary, Alberta, Canada |
| 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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