Before revision:  Inspired by classical techniques for studying the strange attractors of chaotic systems , we introduce a general embedding technique for  time series, consisting of an autoencoder trained with a novel latent-space loss function.
After revision:  Inspired by classical analysis techniques for partial observations of chaotic attractors , we introduce a general embedding technique for time series, consisting of an autoencoder trained with a novel latent-space loss function.