  Using short histories of observations from a dynamical system, a workflow for
the post-training initialization of reservoir computing systems is described.
This strategy is called cold-starting, and it is based on a map called the
starting map, which is determined by an appropriately short history of
observations that maps to a unique initial condition in the reservoir space.
The time series generated by the reservoir system using that initial state can
be used to run the system in autonomous mode, to produce accurate forecasts of
the time series under consideration immediately. By utilizing this map, the
lengthy "washouts" that are necessary to initialize reservoir systems can be
eliminated, enabling the generation of forecasts using any selection of
appropriately short histories of the observations.
