(b) detrended series. The highlighted regions in (a) indicate three examples of striping.[]{data-label="fig:stripes"}](detrendedstripes.jpg "fig:"){width="107mm" height="70mm"}

[0.8]{} ![Hidden time-varying coherence structure of the DAS series in Figure \[fig:stripes\] at selected wavelet scales (resolutions): (a) coherence between series 1 and 2; (b): coherence between series 3 and 4.[]{data-label="fig:stripecoh"}](coherencecasestudy8ch1-2v2.pdf "fig:"){width="104mm"}

[0.8]{} ![Hidden time-varying coherence structure of the DAS series in Figure \[fig:stripes\] at selected wavelet scales (resolutions): (a) coherence between series 1 and 2; (b): coherence between series 3 and 4.[]{data-label="fig:stripecoh"}](coherencecasestudy8ch3-4v2.pdf "fig:"){width="104mm"}

There exist a variety of techniques for the classification of time series in the statistical and machine learning literature. An exhaustive review is beyond the scope of this article, but popular classification methods include hidden Markov models (HMM) (see e.g. [@rabiner89:a; @ephraim02:hidden; @cappe09:inference]); support vector machines [@cortes95:support; @muller01:an; @kampouraki09:heartbeat]; Gaussian mixture models [@mclachlan04:finite; @povinelli04:time; @kersten14:simultaneous]; nearest neigbour classifiers [@zhang04:a; @wei06:semi] and multiscale methods [@chan99:efficient; @morchen03:time; @aykroyd16:classification] to name but a few. More recent contributions for large-scale (online) classification include the MOA machine learning framework [@bifet10:moa; @read12:scalable]. For a recent overview of classification in the time series context, see for example [@fu11:a]. Dependent on the application being considered, one might adopt various modelling choices. For example, some classifiers have distinct advantages, such 