Forecasting timeseries based upon measured data is needed in a wide range of
applications and has been the subject of extensive research. A particularly
challenging task is the forecasting of timeseries generated by chaotic
dynamics. In recent years reservoir computing has been shown to be an effective
method of forecasting chaotic dynamics and reconstructing chaotic attractors
from data. In this work strides are made toward smaller and lower complexity
reservoirs with the goal of improved hardware implementability and more
reliable production of adequate surrogate models. We show that a reservoir of
uncoupled nodes more reliably produces long term timeseries predictions than
complex reservoir topologies. We then link the improved attractor
reconstruction of the uncoupled reservoir with smaller spectral radii of the
resulting surrogate systems. These results indicate that, the node degree plays
an important role in determining whether the desired dynamics will be stable in
the autonomous surrogate system which is attained via closed-loop operation of
the trained reservoir. In terms of hardware implementability, uncoupled nodes
would allow for greater freedom in the hardware architecture because no complex
coupling setups are needed and because, for uncoupled nodes, the system
response is equivalent for space and time multiplexing.

基于测量数据进行时间序列预测在广泛的应用中都是必须的，并且已经成为了广泛研究的主题。近年来，蓄水池计算已被证明是一种有效的方法，用于预测混沌动力学和从数据中重建混沌吸引子。本研究通过减小蓄水池的规模和复杂性，以提高硬件可操作性并更可靠地生产足够的替代模型。研究表明，与复杂的蓄水池拓扑相比，无耦合节点的蓄水池更可靠地产生长期时间序列预测。然后，我们将无耦合蓄水池的改进吸引子重建与所得替代系统的较小谱半径相连接。这些结果表明，节点度在确定所期望的动力学是否稳定于训练蓄水池的闭环操作中的自主替代系统中起着重要作用。就硬件可操作性而言，无耦合节点将允许在硬件架构中具有更大的自由度，因为不需要复杂的耦合设置，并且由于无耦合节点，系统响应对于空间和时间多路复用是等效的。