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

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dc.citation.articleNumber015009en_US
dc.citation.journalTitleMachine Learning: Science and Technologyen_US
dc.citation.volumeNumber3en_US
dc.contributor.authorHoffmann, Moritzen_US
dc.contributor.authorScherer, Martinen_US
dc.contributor.authorHempel, Timen_US
dc.contributor.authorMardt, Andreasen_US
dc.contributor.authorSilva, Brian deen_US
dc.contributor.authorHusic, Brooke E.en_US
dc.contributor.authorKlus, Stefanen_US
dc.contributor.authorWu, Haoen_US
dc.contributor.authorKutz, Nathanen_US
dc.contributor.authorBrunton, Steven L.en_US
dc.contributor.authorNoƩ, Franken_US
dc.date.accessioned2022-03-24T13:31:34Zen_US
dc.date.available2022-03-24T13:31:34Zen_US
dc.date.issued2021en_US
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/.en_US
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.en_US
dc.identifier.digitalHoffmann_2022en_US
dc.identifier.doihttps://doi.org/10.1088/2632-2153/ac3de0en_US
dc.identifier.urihttps://hdl.handle.net/1911/112030en_US
dc.language.isoengen_US
dc.publisherIOP Publishingen_US
dc.rightsOriginal Content from this work may be used under the terms of the Creative Commons Attribution 4.0 licenceen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleDeeptime: a Python library for machine learning dynamical models from time series dataen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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