Extending SSMs with the Exponentially Weighted Signature
The Exponentially Weighted Signature (EWS) generalizes classical path signatures by using bounded linear operators to weight historical data, allowing for richer temporal dynamics like oscillations and growth. This framework provides a continuous-time description of paths that is useful for gradient learning and extends the expressive power of state-space models beyond traditional limits.