Since structural time series models only require fitting process and observation error variance parameters (unlike ARIMA models, for example), the gradient of the log-likelihood function can be computed analytically, providing more efficient parameter estimation than gradient-free ML optimization or EM estimation (which requires repeated smoothing), the two techniques currently implemented here.
I have a very minimal local level + linear regression model with diffuse state initialization up and running at https://github.com/GordStephen/TinyTimeModels.jl - ultimately I think it would be very cool to have a modular structural time series model interface here that would allow for mixing and matching particular components (local level or linear trend random walks, seasonal trends, regression terms, etc).
Since structural time series models only require fitting process and observation error variance parameters (unlike ARIMA models, for example), the gradient of the log-likelihood function can be computed analytically, providing more efficient parameter estimation than gradient-free ML optimization or EM estimation (which requires repeated smoothing), the two techniques currently implemented here.
I have a very minimal local level + linear regression model with diffuse state initialization up and running at https://github.com/GordStephen/TinyTimeModels.jl - ultimately I think it would be very cool to have a modular structural time series model interface here that would allow for mixing and matching particular components (local level or linear trend random walks, seasonal trends, regression terms, etc).