total.

For all scenarios, the generated series randomly switch classes between time segments. A window length of 256 is used when implementing the online dynamic classification method and the training data consists of 10 signals, some of which contain changes in class. The R packages **wavethresh** [@wavethreshpackage] and **mvLSW** [@mvLSWpackage] are used to calculate the wavelet coefficients and transformed coherence that are used in the online dynamic classification.

Long segments of length 300 between class changes are chosen to ensure that there is a maximum of one class change in each dynamic classification window. In the situation where the class changes are reasonably far apart, we expect the online dynamic classification algorithm to classify the signal well. As a contrast, short segments of length 100 are also chosen to demonstrate some potential limitations of the method. In particular, when the signal contains multiple class changes that are close together, there is a possibility that our approach will misclassify the signals.\
For each scenario, we consider a number of examples of generating processes for the classes in the multivariate series. The first example we examine consists of three classes where each class is defined by a trivariate normal signal with mean 