  We propose the use of recurrent neural networks for classifying phases of
matter based on the dynamics of experimentally accessible observables. We
demonstrate this approach by training recurrent networks on the magnetization
traces of two distinct models of one-dimensional disordered and interacting
spin chains. The obtained phase diagram for a well-studied model of the
many-body localization transition shows excellent agreement with previously
known results obtained from time-independent entanglement spectra. For a
periodically-driven model featuring an inherently dynamical time-crystalline
phase, the phase diagram that our network traces in a previously-unexplored
regime coincides with an order parameter for its expected phases.
