Scalable time series causal discovery with approximate causal ordering
File(s)
Published version
Author(s)
Jiao, Ziyang
Guo, Ce
Luk, Wayne
Type
Journal Article
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(m3n) to O(m2n+m3) while keeping the space complexity at O(m2), 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.
Date Issued
2025-10-02
Date Acceptance
2025-10-13
Citation
Mathematics, 2025, 13 (20)
ISSN
2227-7390
Publisher
MDPI AG
Journal / Book Title
Mathematics
Volume
13
Issue
20
Copyright Statement
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/).
License URL
Publication Status
Published
Article Number
3288
Date Publish Online
2025-10-14