Reservoir Computers with Random and Optimized Time-Shifts
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Abstract (arXiv)
We investigate the effects of application of random time-shifts to the readouts of a reservoir computer in terms of both accuracy (training error) and performance (testing error.) For different choices of the reservoir parameters and different `tasks', we observe a substantial improvement in both accuracy and performance. We then develop a simple but effective technique to optimize the choice of the time-shifts, which we successfully test in numerical experiments.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Aug 2021.
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Citing papers per year: 2022: 2; 2023: 2; 2024: 0; 2025: 1.
5 papers.
- 2023Attractor reconstruction with reservoir computers: The effect of the reservoir's conditional Lyapunov exponents on faithful attractor reconstructioncited by 9
- 2022Time Shifts to Reduce the Size of Reservoir Computerscited by 3
- 2022Time-shift selection for reservoir computing using a rank-revealing QR algorithmcited by 1
- 2025Optimizing the Network Topology of a Linear Reservoir Computercited by 1
- 2023Detecting disturbances in network-coupled dynamical systems with machine learningcited by 0
- 2025Optimizing the Network Topology of a Linear Reservoir Computer27 Sep 2025
- 2023Attractor reconstruction with reservoir computers: The effect of the reservoir's conditional Lyapunov exponents on faithful attractor reconstruction30 Dec 2023
- 2023Detecting disturbances in network-coupled dynamical systems with machine learning24 Jul 2023
- 2022Time-shift selection for reservoir computing using a rank-revealing QR algorithm29 Nov 2022
- 2022Time Shifts to Reduce the Size of Reservoir Computers3 May 2022