arXiv:2605.26759v1 Announce Type: new Abstract: Causal discovery from time series is critical for many real-world applications, such as tracing the root causes of anomalies. Existing approaches typically rely on dataset-specific optimization, making it difficult to transfer their causal discovery capabilities to new time series governed by diverse causal mechanisms. In this paper, we propose \textbf{PTCD}, a novel \textbf{P}retraining framework for \textbf{T}ime-series \textbf{C}ausal \textbf{D}iscovery, which improves cross-task generalization through context-conditioned modeling and transfer
The proliferation of complex time-series data in various sectors, from finance to healthcare, necessitates more robust and adaptable causal discovery methods than current dataset-specific approaches.
This development proposes a foundational improvement in how AI systems can identify root causes in dynamic environments, critical for anomaly detection and effective decision-making across numerous applications.
The ability to pretrain causal discovery models will enable faster deployment and better generalization of AI systems for understanding complex, evolving systems, reducing the need for extensive retraining.
- · AI/ML researchers
- · Analytics software providers
- · Industries relying on time-series data (e.g., finance, manufacturing, healthcare
- · Companies relying solely on traditional statistical methods for causal inference
Improved accuracy and efficiency in identifying causal relationships within complex time-series datasets.
Accelerated development of more robust autonomous systems and predictive analytics due to better causality understanding.
Enhanced AI-driven anomaly detection and root cause analysis could significantly prevent systemic failures and optimize operational processes.
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Read at arXiv cs.LG