FTSCommDetector: Discovering Behavioral Communities through Temporal Synchronization

• URL: http://arxiv.org/abs/2510.00014v2
• Date: Thu, 02 Oct 2025 04:20:45 GMT
• Title: FTSCommDetector: Discovering Behavioral Communities through Temporal Synchronization
• Authors: Tianyang Luo, Xikun Zhang, Dongjin Song,
• Abstract summary: Traditional community detection methods fail to capture synchronization-desynchronization patterns where entities move independently yet align during critical moments.<n>We introduce FTSCommDetector, implementing our Temporal Coherence Architecture (TCA) to discover similar and dissimilar communities.<n>As a result, FTSCommDetector achieves consistent improvements across four diverse financial markets.
• Score: 15.690768429709811
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Why do trillion-dollar tech giants AAPL and MSFT diverge into different response patterns during market disruptions despite identical sector classifications? This paradox reveals a fundamental limitation: traditional community detection methods fail to capture synchronization-desynchronization patterns where entities move independently yet align during critical moments. To this end, we introduce FTSCommDetector, implementing our Temporal Coherence Architecture (TCA) to discover similar and dissimilar communities in continuous multivariate time series. Unlike existing methods that process each timestamp independently, causing unstable community assignments and missing evolving relationships, our approach maintains coherence through dual-scale encoding and static topology with dynamic attention. Furthermore, we establish information-theoretic foundations demonstrating how scale separation maximizes complementary information and introduce Normalized Temporal Profiles (NTP) for scale-invariant evaluation. As a result, FTSCommDetector achieves consistent improvements across four diverse financial markets (SP100, SP500, SP1000, Nikkei 225), with gains ranging from 3.5% to 11.1% over the strongest baselines. The method demonstrates remarkable robustness with only 2% performance variation across window sizes from 60 to 120 days, making dataset-specific tuning unnecessary, providing practical insights for portfolio construction and risk management.

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