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The discovery of structure from time series data is a key problem in fields of study working with complex systems.
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Mechanistic theories of causality part i
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Advancing climate science with knowledge-discovery through data mining
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Jon Williamson · 2011
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Vanessa Didelez · 2012
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Learning unknown ode models with gaussian processes
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Causal network reconstruction from time series: From theoretical assumptions to practical estimation
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Neural granger causality for nonlinear time series
Alex Tank, Ian Covert, Nicholas Foti, Ali Shojaie, and Emily Fox · 2018
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Lasso-driven inference in time and space
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On chaotic dynamics in transcription factors and the associated effects in differential gene regulation
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Economy statistical recurrent units for inferring nonlinear granger causality
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Inferring causation from time series in earth system sciences
Jakob Runge, Sebastian Bathiany, Erik Bollt, Gustau Camps-Valls, Dim Coumou, Ethan Deyle, Clark Glymour, Marlene Kretschmer, Miguel D Mahecha, Jordi Muñoz-Marí, et al · 2019
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Feature selection for neural networks using group lasso regularization
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Lasso inference for high-dimensional time series
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Markov equivalence of marginalized local independence graphs
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Odin: Ode-informed regression for parameter and state inference in time-continuous dynamical systems
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On the non-asymptotic and sharp lower tail bounds of random variables
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Policy analysis using synthetic controls in continuous-time
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