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Communication Dans Un Congrès Année : 2023

Rethinking Temporal Dependencies in Multiple Time Series: A Use Case in Financial Data

Jean-Marc Patenaude
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Shengrui Wang
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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.
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hal-04392991 , version 1 (14-01-2024)

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  • HAL Id : hal-04392991 , version 1

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Patrick Asante Owusu, Etienne Tajeuna, Jean-Marc Patenaude, Armelle Brun, Shengrui Wang. Rethinking Temporal Dependencies in Multiple Time Series: A Use Case in Financial Data. IEEE International Conference on Data Mining, Dec 2023, Shanghai (CH), China. ⟨hal-04392991⟩
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