Attention-Enhanced Reservoir Computing
Felix Köster Kazutaka Kanno Jun Ohkubo Atsushi Uchida
Photonic reservoir computing has been recently utilized in time series
forecasting as the need for hardware implementations to accelerate these
predictions has increased. Forecasting chaotic time series remains a
significant challenge, an area where the conventional reservoir computing
framework encounters limitations of prediction accuracy. We introduce an
attention mechanism to the reservoir computing model in the output stage. This
attention layer is designed to prioritize distinct features and temporal
sequences, thereby substantially enhancing the forecasting accuracy. Our
results show that a photonic reservoir computer enhanced with the attention
mechanism exhibits improved forecasting capabilities for smaller reservoirs.
These advancements highlight the transformative possibilities of reservoir
computing for practical applications where accurate forecasting of chaotic time
series is crucial.