Jacobian Regularizer-based Neural Granger Causality Article Swipe
Wanqi Zhou
,
Shuanghao Bai
,
Shujian Yu
,
Qibin Zhao
,
Badong Chen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2405.08779
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2405.08779
With the advancement of neural networks, diverse methods for neural Granger causality have emerged, which demonstrate proficiency in handling complex data, and nonlinear relationships. However, the existing framework of neural Granger causality has several limitations. It requires the construction of separate predictive models for each target variable, and the relationship depends on the sparsity on the weights of the first layer, resulting in challenges in effectively modeling complex relationships between variables as well as unsatisfied estimation accuracy of Granger causality. Moreover, most of them cannot grasp full-time Granger causality. To address these drawbacks, we propose a Jacobian Regularizer-based Neural Granger Causality (JRNGC) approach, a straightforward yet highly effective method for learning multivariate summary Granger causality and full-time Granger causality by constructing a single model for all target variables. Specifically, our method eliminates the sparsity constraints of weights by leveraging an input-output Jacobian matrix regularizer, which can be subsequently represented as the weighted causal matrix in the post-hoc analysis. Extensive experiments show that our proposed approach achieves competitive performance with the state-of-the-art methods for learning summary Granger causality and full-time Granger causality while maintaining lower model complexity and high scalability.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2405.08779
- https://arxiv.org/pdf/2405.08779
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396945086
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- OpenAlex ID
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https://openalex.org/W4396945086Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2405.08779Digital Object Identifier
- Title
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Jacobian Regularizer-based Neural Granger CausalityWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-14Full publication date if available
- Authors
-
Wanqi Zhou, Shuanghao Bai, Shujian Yu, Qibin Zhao, Badong ChenList of authors in order
- Landing page
-
https://arxiv.org/abs/2405.08779Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2405.08779Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2405.08779Direct OA link when available
- Concepts
-
Jacobian matrix and determinant, Granger causality, Econometrics, Causality (physics), Mathematics, Applied mathematics, Computer science, Economics, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.state-of-the-art | 170 |
| abstract_inverted_index.Regularizer-based | 97 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |