a moving window approach, thereby extending previous work to the online dynamic classification setting. This modelling framework allows us to classify multivariate time series with complex dependencies both [*within*]{} and [*between*]{} channels of the series, including those which exhibit visually subtle changes in behaviour over time. Reusing data calculations allows us to also produce a computationally efficient nondecimated wavelet transform in the online setting.

Our article is organised as follows. Section \[sec:background\] contains an overview of the Multivariate Locally Stationary Wavelet (MvLSW) model and existing dynamic classification method. In Section \[sec:method\], we describe the proposed online classification method. Section \[sec:examples\] contains a simulation study evaluating the performance of the proposed classifier using synthetic data, further justifying the use of time-varying coherence as a feature for classification. A case study using an acoustic sensing dataset is then described in Section \[sec:case\], where we discuss the utility of the proposed classifier as a stripe detection method. Finally, Section \[sec:conclusions\] includes some concluding remarks.

Wavelets and time series {#sec:background}
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The problem of modelling and analysing non-stationary time series can be approached in a number of ways that often involve assuming the changing second-order structure adopts a time varying spectrum or autocovariance. Examples 