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 space play important roles in enabling accurate predictions.We establish simple post-processing methods that train on Memorial Bench Keepsake 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 randomly recalls previous states of reservoir nodes.The use of multi-random-timeshifting allows for smaller Coffee Maker (Built-In Grinder/Glass Carafe) 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.