Large-scale nonlinear Granger causality: A data-driven, multivariate approach to recovering directed networks from short time-series data

• URL: http://arxiv.org/abs/2009.04681v1
• Date: Thu, 10 Sep 2020 06:27:57 GMT
• Title: Large-scale nonlinear Granger causality: A data-driven, multivariate approach to recovering directed networks from short time-series data
• Authors: Axel Wism\"uller, Adora M. DSouza and Anas Z. Abidin
• Abstract summary: We introduce a large-scale Granger Causality (lsNGC) approach for inferring causal interactions between system components. lsNGC identifies casual relations with no explicit a priori assumptions on functional interdependence between component time-series. We extensively study the ability of lsNGC to recovering network structure from two-node to thirty-four node chaotic time-series systems.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To gain insight into complex systems it is a key challenge to infer nonlinear causal directional relations from observational time-series data. Specifically, estimating causal relationships between interacting components in large systems with only short recordings over few temporal observations remains an important, yet unresolved problem. Here, we introduce a large-scale Nonlinear Granger Causality (lsNGC) approach for inferring directional, nonlinear, multivariate causal interactions between system components from short high-dimensional time-series recordings. By modeling interactions with nonlinear state-space transformations from limited observational data, lsNGC identifies casual relations with no explicit a priori assumptions on functional interdependence between component time-series in a computationally efficient manner. Additionally, our method provides a mathematical formulation revealing statistical significance of inferred causal relations. We extensively study the ability of lsNGC to recovering network structure from two-node to thirty-four node chaotic time-series systems. Our results suggest that lsNGC captures meaningful interactions from limited observational data, where it performs favorably when compared to traditionally used methods. Finally, we demonstrate the applicability of lsNGC to estimating causality in large, real-world systems by inferring directional nonlinear, multivariate causal relationships among a large number of relatively short time-series acquired from functional Magnetic Resonance Imaging (fMRI) data of the human brain.

Related papers

• Causal Discovery from Time-Series Data with Short-Term Invariance-Based Convolutional Neural Networks [12.784885649573994]
  Causal discovery from time-series data aims to capture both intra-slice (contemporaneous) and inter-slice (time-lagged) causality. We propose a novel gradient-based causal discovery approach STIC, which focuses on textbfShort-textbfTerm textbfInvariance using textbfConvolutional neural networks.
  arXiv  Detail & Related papers  (2024-08-15T08:43:28Z)
• Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values [67.76168547245237]
  We introduce a novel framework called GST-Pro, which utilizes a graphtemporal process and anomaly scorer to detect anomalies. Our experimental results show that the GST-Pro method can effectively detect anomalies in time series data and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-11T10:10:16Z)
• Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection [67.60791405198063]
  We propose a correlation-aware spatial-temporal graph learning (termed CST-GL) for time series anomaly detection. CST-GL explicitly captures the pairwise correlations via a multivariate time series correlation learning module. A novel anomaly scoring component is further integrated into CST-GL to estimate the degree of an anomaly in a purely unsupervised manner.
  arXiv  Detail & Related papers  (2023-07-17T11:04:27Z)
• Causal discovery for time series with constraint-based model and PMIME measure [0.0]
  We present a novel approach for discovering causality in time series data that combines a causal discovery algorithm with an information theoretic-based measure. We evaluate the performance of our approach on several simulated data sets, showing promising results.
  arXiv  Detail & Related papers  (2023-05-31T09:38:50Z)
• Causality-Based Multivariate Time Series Anomaly Detection [63.799474860969156]
  We formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data. We then propose a causality-based anomaly detection approach, which first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism. We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications.
  arXiv  Detail & Related papers  (2022-06-30T06:00:13Z)
• Causal Discovery from Conditionally Stationary Time Series [18.645887749731923]
  State-Dependent Causal Inference (SDCI) is able to recover the underlying causal dependencies, provably with fully-observed states and empirically with hidden states. improved results over non-causal RNNs on modeling NBA player movements demonstrate the potential of our method.
  arXiv  Detail & Related papers  (2021-10-12T18:12:57Z)
• Learning Neural Causal Models with Active Interventions [83.44636110899742]
  We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting. We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
  arXiv  Detail & Related papers  (2021-09-06T13:10:37Z)
• Leveraging Pre-Images to Discover Nonlinear Relationships in Multivariate Environments [0.0]
  Causal discovery offers a crucial functionality in scientific discovery using artificial intelligence. It became apparent that many real-world temporal observations are nonlinearly related to each other. We show that our method outperforms state-of-the-art causal discovery methods when the observations are restricted by time and are nonlinearly related.
  arXiv  Detail & Related papers  (2021-06-01T22:42:51Z)
• Consistency of mechanistic causal discovery in continuous-time using Neural ODEs [85.7910042199734]
  We consider causal discovery in continuous-time for the study of dynamical systems. We propose a causal discovery algorithm based on penalized Neural ODEs.
  arXiv  Detail & Related papers  (2021-05-06T08:48:02Z)
• Efficient Causal Inference from Combined Observational and Interventional Data through Causal Reductions [68.6505592770171]
  Unobserved confounding is one of the main challenges when estimating causal effects. We propose a novel causal reduction method that replaces an arbitrary number of possibly high-dimensional latent confounders. We propose a learning algorithm to estimate the parameterized reduced model jointly from observational and interventional data.
  arXiv  Detail & Related papers  (2021-03-08T14:29:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.