Chaotic time series prediction is a research topic in both theoretical and real-life area. Its aim is to predict the future of the time series based on past observations. Reservoir computing (RC) is a promising tool widely used in time series prediction. Short-term memory (STM) is very important to model time-dependent time series by the RC approach. However, traditional RC hardly achieves sufficient STM capacity required by a complicated time series prediction task. For this reason, this paper proposes an asynchronous deep RC (ADRC), which is composed of a number of sub-reservoirs that are connected one by one in sequence. Moreover, delayed modules are inserted between every two adjacent sub-reservoirs. The sub-reservoirs in the proposed ADRC preserve the input characteristics by a relay mode and deal with them asynchronously. This makes the reservoir achieve large STM capacity and rich dynamics. The experimental results demonstrate that the proposed ADRC is prominent in modeling chaotic time series signals with high performance.

混沌时间序列预测是理论和现实生活领域的研究课题。其目的是根据过去的观察结果预测时间序列的未来。储层计算（RC）是广泛用于时间序列预测的有前途的工具。短期记忆（STM）对于通过RC方法建模与时间相关的时间序列非常重要。但是，传统的RC很难达到复杂的时间序列预测任务所需的足够的STM容量。因此，本文提出了一种异步深层RC（ADRC），它由许多子油库组成，这些子油库按顺序一个接一个地连接。此外，延迟模块插入在每两个相邻的子油箱之间。拟议的ADRC中的子水库通过中继模式保留输入特征并异步处理它们。这使储层实现了较大的STM容量和丰富的动力学特性。实验结果表明，所提出的ADRC在高性能的混沌时间序列信号建模中具有突出的地位。