pypfilt

If there is a system or process that can be:

• Described (modelled) with mathematical equations; and

• Measured repeatedly in some (noisy) way.

Then you can use pypfilt to estimate the state and/or parameters of this system.

The Getting Started guide introduces the Lorenz-63 system of ordinary differential equations, which has chaotic solutions for some parameter values and initial states. This guide demonstrates:

• How to build a simulation model for the Lorenz-63 system;

• How to generate simulated observations from this model;

• How to fit the simulation model to observations; and

• How to generate forecasts that predict the future behaviour of the Lorenz-63 system.

The How-to Guides cover a range of further topics, including how to generate forecasts with:

• Continuous-time Markov chain (CTMC) models;

• Discrete-time Markov chain (DTMC) models;

• Ordinary differential equation (ODE) models; and

• Stochastic differential equation (SDE) models.