  We present an algorithm for the visualisation of time series. To that end we
employ echo state networks to convert time series into a suitable vector
representation which is capable of capturing the latent dynamics of the time
series. Subsequently, the obtained vector representations are put through an
autoencoder and the visualisation is constructed using the activations of the
bottleneck. The crux of the work lies with defining an objective function that
quantifies the reconstruction error of these representations in a principled
manner. We demonstrate the method on synthetic and real data.
