[@Li2007] that performs a non-uniform bin assignment such that the distribution of the bin frequency roughly follows a Gaussian distribution. As such, setting the right number of bins is a less critical task, since the risk of sparsity is drastically reduced. An arbitrarily large number of bins can be used, and the choice mostly depends on the computational requirements for computing the transitions.

Recurrence Plot (RP)
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The recurrence plot (RP) [@kamphorst1987recurrence] transforms a time series into a matrix of recurrences to reveal in which points some trajectories return to a previously visited state. In this paper, we follow a non-binarized version of RP, as proposed by [@souza2014extracting]. The RP encoding consists in the following transformation: $$\mathrm{RP}_{ij} = ||S_i-S_j||,\quad i,j\in \llbracket1,K\rrbracket,$$ where the time series is split into $K$ sub-sequences $(S_i)_{i\in \llbracket1,K\rrbracket}$. Here, we set the sub-sequence length to $1$ (we only consider the difference between single values), and defined $K=l_2$.

**Proposed modification:** This transformation is non-reversible and also looses information on the values in the time series since only the differences in values are kept. We propose, therefore, the following transformation: $$\widehat{\mathrm{RP}} = \mathrm{RP} + \mathrm{mean}(X).$$ This transformation aims at capturing recurrences in individual time series around their mean.

