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

Contrary to the problem of detection, in which a decision is to be made about the presence or absence of a pattern, the problem of localization assumes that the pattern is present and its precise location is to be retrieved. The temporal nature of the data acquisition process complicates these tasks, as it causes the shape of the patterns of interest to suffer deformations in time known as warps. For pattern detection problems, many techniques exist based on aligning the query time series to a known reference pattern, commonly through dynamic time warping (DTW) [5]. Similarly, a common pattern localization technique consists in aligning the query time series to a reference time series that contains several patterns of interest [3]. The work of Steven Van Vaerenbergh was supported by the Ministerio de Economía, Industria y Competitividad (MINECO) of Spain under grant TEC2014-57402-JIN (PRISMA). The work of Víctor Elvira was supported by the Agence Nationale de la Recherche of France under PISCES project (ANR-17-CE40-0031-01).