Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space
This addresses pattern localization in time series for applications like non-destructive testing, but it is incremental as it builds on existing alignment techniques.
The paper tackles the problem of locating predefined patterns in time series by aligning them with a theoretical model, proposing a method that maps time series into a latent correlation space to increase similarity before alignment. Experiments on non-destructive testing data show significant improvements over state-of-the-art methods.
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.