# Reservoir Computers with Random and Optimized Time-Shifts

arXiv: https://arxiv.org/abs/2108.12765 · HTML: https://mlsanity.com/paper/2108.12765/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2021.

## Built on by (most cited first, 5 of 5)

- [Attractor reconstruction with reservoir computers: The effect of the reservoir's conditional Lyapunov exponents on faithful attractor reconstruction](https://mlsanity.com/paper/2401.00885/) (2023)
- [Time Shifts to Reduce the Size of Reservoir Computers](https://mlsanity.com/paper/2205.02267/) (2022)
- [Time-shift selection for reservoir computing using a rank-revealing QR algorithm](https://mlsanity.com/paper/2211.17095/) (2022)
- [Optimizing the Network Topology of a Linear Reservoir Computer](https://mlsanity.com/paper/2509.23391/) (2025)
- [Detecting disturbances in network-coupled dynamical systems with machine learning](https://mlsanity.com/paper/2307.12771/) (2023)

## 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.