A Sequential Monte Carlo (SMC, or "particle filter") Python package for online model estimation and probabilistic forecasting.
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.