An asynchronously deep reservoir computing for predicting chaotic time series

Abstract

Abstract 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.