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 representa- tions 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.
['Nikolaos Gianniotis', 'Dennis Kügler', 'Peter Tino', 'Kai Lars Polsterer', 'Ranjeev Misra']
Autoencoding Time Series for Visualisation