as simplicity of implementation, speed or suitability for massive online applications. However many, such as GMM or SVM-based approaches, do not explicitly allow temporal dependence or are limited to a narrow class of series structure (HMMs), which is seen as crucial to classification of time series in the majority of realistic settings (see e.g. [@bifet13:pitfalls]). Complex hidden dependence structure is typical of the DAS data studied in this article (see Figure \[fig:stripecoh\]).

Our approach to the dynamic stripe identification problem builds on recent work within the time series literature. Wavelet approaches to modelling time series have become very popular in recent years, principally because of their ability to provide time-localised measures of the spectral content inherent within many contemporary data (e.g. [@KEJ2013; @NAEK2015; @ChauvonSachs2015; @NPES2017SeriesA]). This [*locally stationary*]{} modelling paradigm is flexible enough to represent a wide range of non-stationary behaviour and has also been extended to enable the modelling and estimation of multivariate non-stationary time series structures (e.g. [@SFJ2010] and [@Park]). Typically these settings assume that the data have already been collected, and are available for offline analyses.

The novel contribution in this article is to employ the MvLSW modelling framework of [@Park] to represent the DAS data, using 