Empirical evaluation of the cycle reservoir with regular jumps for time series forecasting: A comparison study

Abstract

The cycle reservoir with regular jumps (CRJ) is a recent deterministic reservoir model with a very simple structure and highly constrained weight values. CRJ was proposed as an alternative to the randomized Echo State Network (ESN) reservoir. In this work, we empirically evaluate the performance of CRJ for time series forecasting problems, and compare it to ESN and Auto-Regressive with eXogenous inputs (NARX) models. The comparison is conducted based on seven time series datasets that represent different real world cases. Simulation results show that CRJ outperforms ESN and NARX models. The results also demonstrate the effectiveness of CRJ when applied for different time series forecasting problems.