Recent research has established the effectiveness of machine learning for data-driven prediction of the future evolution of unknown dynamical systems, including chaotic systems. However, these approaches require large amounts of measured time series data from the process to be predicted. When only limited data is available, forecasters are forced to impose significant model structure that may or may not accurately represent the process of interest. In this work, we present a Meta-learning Approach to Reservoir Computing (MARC), a data-driven approach to automatically extract an appropriate model structure from experimentally observed "related" processes that can be used to vastly reduce the amount of data required to successfully train a predictive model. We demonstrate our approach on a simple benchmark problem, where it beats the state of the art meta-learning techniques, as well as a challenging chaotic problem.

翻译：最近的研究已经确立了对数据驱动的对未知动态系统,包括混乱系统的今后演变进行数据驱动预测的机器学习的有效性,然而,这些方法需要从这一过程中预测大量测量的时间序列数据。当只有有限的数据时,预报人员被迫强加可能或可能不能准确代表感兴趣的过程的重要模型结构。在这项工作中,我们提出了一个“回收计算元学习方法”(MARC),一种数据驱动方法,从实验观测到的“相关”流程中自动提取适当的模型结构,可以用来大量减少成功培训预测模型所需的数据数量。我们展示了我们对于一个简单基准问题的方法,即它战胜了现代学习技术的状态,以及一个具有挑战性的混乱问题。