R code for forecasting with the bootstrap filter

In one of my previous blog posts I demonstrated how to implement a particle  (bootstrap) filter in R.
In this post I will demonstrate how to predict future system states and observations with the particle/bootstrap filter.

Bootstrap filter forecasting

Continue reading R code for forecasting with the bootstrap filter

R code for implementing a particle filter

The R code below implements a particle filter in R. The implemented particle filter is also referred to as the bootstrap filter.
If necessary, the implemented bootstrap filter performs resampling and/or roughening.

Similar to the Extended Kalman Filter (EKF), the Unscented Kalman Filter (UKF), the Ensemble Kalman Filter (EnKF), and the Gauss-Hermite Kalman Filter (GHKF), the bootstrap filter may be used for solving nonlinear filtering problems.

Particle filter

Continue reading R code for implementing a particle filter

R code for forecasting with the Gauss-Hermite Kalman filter

In one of my previous blog posts I showed how to implement and apply the Gauss-Hermite Kalman Filter (GHKF) in R.
In this post I will demonstrate how to predict future system states and observations with the GHKF.

Gauss-Hermite Kalman filter forecasting

Continue reading R code for forecasting with the Gauss-Hermite Kalman filter

R code for estimating the parameters of a Gauss-Hermite Kalman filter model using likelihood maximization

In one of my previous blog posts I demonstrated how to implement and apply the Gauss-Hermite Kalman Filter (GHKF) in R.
In this post I will show how to fit unknown parameters of a GHKF model by means of likelihood maximization.

Gauss-Hermite Kalman filter Lorenz system

Continue reading R code for estimating the parameters of a Gauss-Hermite Kalman filter model using likelihood maximization