Python framework for Stochastic Differential Equations modeling
This package implements functionalities for working with Stochastic Differential Equations models (SDEs for short).
It includes simulation routines as well as estimation methods based on observed time series.
Conceptually the information required to describe an SDEs can be divided in three groups: model, sampling and data.
The sdelearn class is the main class of this package, dedicated to the interaction with the user.
How to create a sdelearn class?
A sdelearn class is based on three dedicated subclasses, SdeModel, SdeSampling and SdeData,
containing information about the model, the sampling structure and the observed data respectively. First these three classes
must be created:
-
SdeModel: contains information about the Sde model, in particular the “actual” Sde formula. It is assumed to be a parametric
model,