Abstract Reservoir computing is a machine learning method that is well-suited for complex time series prediction tasks.Both delay embedding and the projection of input data into a higher-dimensional usc trojans snapback hat space play important roles in enabling accurate predictions.We establish simple post-processing methods that train on past node states at uniformly or randomly-delayed timeshifts.These methods improve reservoir computer prediction performance through increased feature dimension and/or better delay embedding.Here we introduce the multi-random-timeshifting method that sex shop arles randomly recalls previous states of reservoir nodes.
The use of multi-random-timeshifting allows for smaller reservoirs while maintaining large feature dimensions, is computationally cheap to optimise, and is our preferred post-processing method.For experimentalists, all our post-processing methods can be translated to readout data sampled from physical reservoirs, which we demonstrate using readout data from an experimentally-realised laser reservoir system.