Scalable Time Series Causal Discovery with Approximate Causal Ordering

Ziyang Jiao, Ce Guo () and Wayne Luk
Additional contact information
Ziyang Jiao: Department of Computing, Imperial College London, London SW7 2AZ, UK
Ce Guo: Department of Computing, Imperial College London, London SW7 2AZ, UK
Wayne Luk: Department of Computing, Imperial College London, London SW7 2AZ, UK

Mathematics, 2025, vol. 13, issue 20, 1-15

Abstract: Causal discovery in time series data presents a significant computational challenge. Standard algorithms are often prohibitively expensive for datasets with many variables or samples. This study introduces and validates a heuristic approximation of the VarLiNGAM algorithm to address this scalability problem. The standard VarLiNGAM method relies on an iterative refinement procedure for causal ordering that is computationally expensive. Our heuristic modifies this procedure by omitting the iterative refinement. This change permits a one-time precomputation of all necessary statistical values. The algorithmic modification reduces the time complexity of VarLiNGAM from O ( m 3 n ) to O ( m 2 n + m 3 ) while keeping the space complexity at O ( m 2 ) , where m is the number of variables and n is the number of samples. While an approximation, our approach retains VarLiNGAM’s essential structure and empirical reliability. On large-scale financial data with up to 400 variables, our algorithm achieves up to a 13.36× speedup over the standard implementation and an approximate 4.5× speedup over a GPU-accelerated version. Evaluations across medical time series analysis, IT service monitoring, and finance demonstrate the heuristic’s robustness and practical scalability. This work offers a validated balance between computational efficiency and discovery quality, making large-scale causal analysis feasible on personal computers.

Keywords: causal discovery; time series; scalability; VarLiNGAM (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/20/3288/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/20/3288/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:20:p:3288-:d:1771290

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().