Keyword Analysis & Research: granger causality machine learning


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Frequently Asked Questions

Do neural Granger causality methods outperform state-of-the-art nonlinear Granger causal methods?

We show that our neural Granger causality methods outperform state-of-the-art nonlinear Granger causality methods on the DREAM3 challenge data. This data consists of nonlinear gene expression and regulation time courses with only a limited number of time points.

Are Granger interactions linear or nonlinear?

While most classical approaches to Granger causality detection assume linear dynamics, many interactions in real-world applications, like neuroscience and genomics, are inherently nonlinear. In these cases, using linear models may lead to inconsistent estimation of Granger causal interactions.

How does the CMLP model detect Granger non-causality?

During training, sparse penalties on the input layer's weight matrix set groups of parameters to zero, which can be interpreted as discovering Granger non-causality. The cMLP model can be trained with three different penalties: group lasso, group sparse group lasso, and hierarchical.

Can deep learning be used on nonlinear gene expression and regulation time courses?

This data consists of nonlinear gene expression and regulation time courses with only a limited number of time points. The successes we show in this challenging dataset provide a powerful example of how deep learning can be useful in cases that go beyond prediction on large datasets.


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