• Corpus ID: 59446452

Neural Granger Causality for Nonlinear Time Series

@article{Tank2018NeuralGC,
  title={Neural Granger Causality for Nonlinear Time Series},
  author={Alex Tank and Ian Covert and Nicholas J. Foti and Ali Shojaie and Emily B. Fox},
  journal={arXiv: Machine Learning},
  year={2018}
}
While most classical approaches to Granger causality detection assume linear dynamics, many interactions in applied domains, like neuroscience and genomics, are inherently nonlinear. In these cases, using linear models may lead to inconsistent estimation of Granger causal interactions. We propose a class of nonlinear methods by applying structured multilayer perceptrons (MLPs) or recurrent neural networks (RNNs) combined with sparsity-inducing penalties on the weights. By encouraging specific… 

Economy Statistical Recurrent Units For Inferring Nonlinear Granger Causality

This work makes a case that the network topology of Granger causal relations is directly inferrable from a structured sparse estimate of the internal parameters of the SRU networks trained to predict the processes’ time series measurements.

Echo State Network models for nonlinear Granger causality

ES-GC performs better than commonly used and recently developed GC detection approaches, making it a valuable tool for the analysis of e.g. multivariate biological networks, and explores the structure of ES-GC networks in the human brain employing functional MRI data.

Granger-Causal Link Discovery in Large Temporal Networks through Conditional Models

This paper proposes to learn time-lagged model parameters with the objective of improving recall of links, while learning to defer predictions when the overlap assumption is violated over observed time series to demonstrate a 25% increase in the area under the precision-recall curve for discovering Grangercausal links.

Recurrent neural networks for reconstructing complex directed brain connectivity

  • A. DuggentoM. GuerrisiN. Toschi
  • Computer Science
    2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
  • 2019
The ability of the ESN-based Granger Causality (ES-GC) to capture nonlinear causal relations by simulating multivariate coupling in a network of nonlinearly interacting, noisy Duffing oscillators operating in a chaotic regime is characterized.

Causal Inference in Non-linear Time-series using Deep Networks and Knockoff Counterfactuals

This paper proposes to use deep autoregressive networks (DeepAR) in tandem with counterfactual analysis to infer nonlinear causal relations in multivariate time series, and extends the concept of Granger causality using probabilistic forecasting with DeepAR.

A Parsimonious Granger Causality Formulation for Capturing Arbitrarily Long Multivariate Associations

A generalization of autoregressive models for GC estimation based on Wiener–Volterra decompositions with Laguerre polynomials as basis functions is presented, showing that it is able to reproduce current knowledge as well as to uncover previously unknown directed influences between cortical and limbic brain regions.

Interpretable Models for Granger Causality Using Self-explaining Neural Networks

This paper proposes a novel framework for inferring multivariate Granger causality under nonlinear dynamics based on an extension of self-explaining neural networks that is more interpretable than other neural-network-based techniques for inference.

Non-Asymptotic Guarantees for Robust Identification of Granger Causality via the LASSO

It is established that the sufficient conditions of LASSO also suffice for robust identification of Granger causal influences, and the false positive error probability of a simple thresholding rule for identifying Granger causal effects is characterized.

Forecasting, Causality, and Impulse Response with Neural Vector Autoregressions

A vector autoencoder nonlinear autoregression neural network (VANAR) capable of both automatic time series feature extraction for its inputs and functional form estimation and significantly outperforms VAR in the forecast and causality tests.
...

References

SHOWING 1-10 OF 33 REFERENCES

Kernel-Granger causality and the analysis of dynamical networks.

The proposed method of analysis of dynamical networks based on a recent measure of Granger causality between time series is applied to a network of chaotic maps and to a simulated genetic regulatory network and it is shown that the underlying topology of the network can be reconstructed from time series of node's dynamics, provided that a sufficient number of samples is available.

Inferring connectivity in networked dynamical systems: Challenges using Granger causality.

The results show a significant systematic disparity between the original and inferred network, unless the true structure is extremely sparse or dense, and advocate the need to perform comparisons with any network inference method since the inferred connectivity results appear to have very little to do with the ground truth system.

Grouped graphical Granger modeling for gene expression regulatory networks discovery

A novel methodology, which overcomes the limitations of existing methods in computational biology by applying a regression method suited for high-dimensional and large data, and by leveraging the group structure among the lagged temporal variables according to the time series they belong to is proposed.

Grouped graphical Granger modeling methods for temporal causal modeling

A novel enhancement to the graphical Granger methodology is proposed by developing and applying families of regression methods that are sensitive to group information among variables, to leverage the group structure present in the lagged temporal variables according to the time series they belong to.

Hierarchical Vector Autoregression

A new class of regularized VAR models are proposed, called hierarchical vector autoregression (HVAR), that embed the notion of lag selection into a convex regularizer, and provide computationally efficient algorithms for solving HVAR problems that can be parallelized across the components.

Network granger causality with inherent grouping structure

This work aims to learn a network structure from temporal panel data, employing the framework of Granger causal models under the assumptions of sparsity of its edges and inherent grouping structure among its nodes, and introduces a group lasso regression regularization framework.

Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality

It is shown how high-dimensional causal inference tasks can be naturally cast as sparse function estimation problems, leading to novel nonlinear extensions of a class of Graphical Granger Causality techniques.

Inferring Dynamic Genetic Networks with Low Order Independencies

  • Sophie Lèbre
  • Computer Science
    Statistical applications in genetics and molecular biology
  • 2009
This paper proposes to approximate DAG G by considering low order conditional independencies, and introduces partial qth order conditional dependence DAGs G(q) and analyzes their probabilistic properties.

A Clockwork RNN

This paper introduces a simple, yet powerful modification to the simple RNN architecture, the Clockwork RNN (CW-RNN), in which the hidden layer is partitioned into separate modules, each processing inputs at its own temporal granularity, making computations only at its prescribed clock rate.

New Introduction to Multiple Time Series Analysis

This reference work and graduate level textbook considers a wide range of models and methods for analyzing and forecasting multiple time series. The models covered include vector autoregressive,