Rethinking Temporal Dependencies in Multiple Time Series: A Use Case in Financial Data
Résumé
These days, complex systems yield copious time series data, necessitating understanding co-generation, often assessed through pairwise comparisons. However, this method lacks scalability and temporal dynamics handling. In this paper, we advocate using a temporal graph to capture contiguous effects among multiple time series efficiently. Our two-step approach identifies patterns and temporal influences with low execution time, showcasing its potential in financial system incident prediction.
| Origine | Fichiers produits par l'(les) auteur(s) |
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