Nowcasting using regression on signatures

Understand

We introduce a new method of nowcasting using regression on path signatures.

• Path signatures capture the geometric properties of sequential data.
• Because signatures embed observations in continuous time, they naturally handle mixed frequencies and missing data.
• We prove theoretically, and with simulations, that regression on signatures subsumes the linear Kalman filter and retains desirable consistency properties.