Feature-Based Echo-State Networks: A Step Towards Interpretability and
  Minimalism in Reservoir Computer
Debdipta Goswami
This paper proposes a novel and interpretable recurrent neural-network
structure using the echo-state network (ESN) paradigm for time-series
prediction. While the traditional ESNs perform well for dynamical systems
prediction, it needs a large dynamic reservoir with increased computational
complexity. It also lacks interpretability to discern contributions from
different input combinations to the output. Here, a systematic reservoir
architecture is developed using smaller parallel reservoirs driven by different
input combinations, known as features, and then they are nonlinearly combined
to produce the output. The resultant feature-based ESN (Feat-ESN) outperforms
the traditional single-reservoir ESN with less reservoir nodes. The predictive
capability of the proposed architecture is demonstrated on three systems: two
synthetic datasets from chaotic dynamical systems and a set of real-time
traffic data.