PyDaddy: A Python package for discovering stochastic dynamical equations from timeseries data. (arXiv:2205.02645v1 [q-bio.QM])

Web: http://arxiv.org/abs/2205.02645

Most real-world ecological dynamics, ranging from ecosystem dynamics to
collective animal movement, are inherently stochastic in nature. Stochastic
differential equations (SDEs) are a popular modelling framework to model
dynamics with intrinsic randomness. Here, we focus on the inverse question: If
one has empirically measured time-series data from some system of interest, is
it possible to discover the SDE model that best describes the data. Here, we
present PyDaddy (PYthon library for DAta Driven DYnamics), a toolbox to
construct and analyze …

arxiv bio data package python stochastic timeseries