Grigoryeva, LyudmilaGrigoryeva, LyudmilaKemeth, Felix P.Kevrekidis, YannisManjunath, GandhiOrtega, Juan-PabloSteynberg, Matthys J.2025-04-242025-04-242024-12Grigoryeva, L., Hamzi, B., Kemeth, F.P. et al. 2024, 'Data-driven cold starting of good reservoirs', Physica D, vol. 469, art. 134325, pp. 1-12. https://DOI.org/10.1016/j.physd.2024.134325.0167-2789 (print)1872-8022 (online)10.1016/j.physd.2024.134325http://hdl.handle.net/2263/102212DATA AVAILABILITY : We included the link to the GitHub folder that contains the code and data used in the paper.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 in order 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.en© 2024 The Author(s). This is an open access article under the CC BY license.Reservoir computingGeneralized synchronizationStarting mapForecastingPath continuationDynamical systemsSDG-04: Quality educationSDG-06: Clean water and sanitationSDG-09: Industry, innovation and infrastructureArticle