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PyDaddy: A Python package for discovering stochastic dynamical equations from timeseries data. (arXiv:2205.02645v2 [q-bio.QM] UPDATED)
Nov. 4, 2022, 1:12 a.m. | Arshed Nabeel, Ashwin Karichannavar, Shuaib Palathingal, Jitesh Jhawar, Danny Raj M, Vishwesha Guttal
cs.LG updates on arXiv.org arxiv.org
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 …
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