Implementing a particle filter in C++ using Rcpp (part 2)

In my previous blog post I showed how to implement a particle filter in C++.

In this post I will implement a backward-simulation particle smoother (see Example 3 in the code below) and a method for forecasting with the particle filter (Example 4) in C++. By using R packages Rcpp (version 0.12.11) and RcppArmadillo (version 0.7.800.2.0) it is possible to subsequently run this smoother and forecasting method in R. Continue reading Implementing a particle filter in C++ using Rcpp (part 2)

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 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