used to efficiently update the wavelet coefficients and the transformed coherence estimate for the new window. Using the information previously calculated from the training signals, we can then obtain the probability that the signal belongs to a particular class for the time points contained in the new window. The algorithm continues by repeatedly moving the window for each new data point and estimating the probability of each data point belonging to a class until we reach the end of the data stream.

During our classification algorithm, we obtain multiple estimates for the probability that a signal belongs to a particular class (at each time point) from the different windows into which a data point falls. For example, for a time series of length $T$ analysed with a moving window of length $w<T$, we obtain $w$ estimates for the probabilities of an individual time point $t$ belonging to a given class $c$, which we denote $p_{t,i}^{(c)}$ for window $i$.

A question that arises as a result of the iterative approach is how to combine the estimates from different windows to obtain an overall probability that the time point belongs to a particular class, and hence classify the signal. In what follows, 