Deeptime: a Python library for machine learning dynamical models from time series data

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dc.citation.articleNumber015009
dc.citation.journalTitleMachine Learning: Science and Technology
dc.citation.volumeNumber3
dc.contributor.authorHoffmann, Moritz
dc.contributor.authorScherer, Martin
dc.contributor.authorHempel, Tim
dc.contributor.authorMardt, Andreas
dc.contributor.authorSilva, Brian de
dc.contributor.authorHusic, Brooke E.
dc.contributor.authorKlus, Stefan
dc.contributor.authorWu, Hao
dc.contributor.authorKutz, Nathan
dc.contributor.authorBrunton, Steven L.
dc.contributor.authorNoƩ, Frank
dc.date.accessioned2022-03-24T13:31:34Z
dc.date.available2022-03-24T13:31:34Z
dc.date.issued2021
dc.description.abstractGeneration and analysis of time-series data is relevant to many quantitative fields ranging from economics to fluid mechanics. In the physical sciences, structures such as metastable and coherent sets, slow relaxation processes, collective variables, dominant transition pathways or manifolds and channels of probability flow can be of great importance for understanding and characterizing the kinetic, thermodynamic and mechanistic properties of the system. Deeptime is a general purpose Python library offering various tools to estimate dynamical models based on time-series data including conventional linear learning methods, such as Markov state models (MSMs), Hidden Markov Models and Koopman models, as well as kernel and deep learning approaches such as VAMPnets and deep MSMs. The library is largely compatible with scikit-learn, having a range of Estimator classes for these different models, but in contrast to scikit-learn also provides deep Model classes, e.g. in the case of an MSM, which provide a multitude of analysis methods to compute interesting thermodynamic, kinetic and dynamical quantities, such as free energies, relaxation times and transition paths. The library is designed for ease of use but also easily maintainable and extensible code. In this paper we introduce the main features and structure of the deeptime software. Deeptime can be found under https://deeptime-ml.github.io/.
dc.identifier.citationHoffmann, Moritz, Scherer, Martin, Hempel, Tim, et al.. "Deeptime: a Python library for machine learning dynamical models from time series data." <i>Machine Learning: Science and Technology,</i> 3, (2021) IOP Publishing: https://doi.org/10.1088/2632-2153/ac3de0.
dc.identifier.digitalHoffmann_2022
dc.identifier.doihttps://doi.org/10.1088/2632-2153/ac3de0
dc.identifier.urihttps://hdl.handle.net/1911/112030
dc.language.isoeng
dc.publisherIOP Publishing
dc.rightsOriginal Content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleDeeptime: a Python library for machine learning dynamical models from time series data
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
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