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FTSCommDetector: Discovering Behavioral Communities through Temporal Synchronization

Tianyang Luo, Xikun Zhang, Dongjin Song

TL;DR

The paper tackles the challenge of uncovering behavioral communities in continuous multivariate time series by arguing that traditional, snapshot-based clustering misses synchronization patterns that emerge during market disruptions. It introduces FTSCommDetector and the Temporal Coherence Architecture (TCA), which integrates scale-adaptive encoding, NAV-based edge embeddings, and three-stream fusion to jointly model multi-scale temporal dynamics and static network structure. Empirical results across SP100, SP500, SP1000, and Nikkei 225 show consistent improvements in intra-cluster coherence and inter-cluster dissimilarity, with window-size robustness and case studies illustrating regime detection during events like GameStop and AI valuation corrections. The approach provides a scalable, data-driven pathway for dynamic portfolio construction and risk management by revealing hidden, temporally coherent behavioral communities that transcend static sector classifications.

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.

FTSCommDetector: Discovering Behavioral Communities through Temporal Synchronization

TL;DR

The paper tackles the challenge of uncovering behavioral communities in continuous multivariate time series by arguing that traditional, snapshot-based clustering misses synchronization patterns that emerge during market disruptions. It introduces FTSCommDetector and the Temporal Coherence Architecture (TCA), which integrates scale-adaptive encoding, NAV-based edge embeddings, and three-stream fusion to jointly model multi-scale temporal dynamics and static network structure. Empirical results across SP100, SP500, SP1000, and Nikkei 225 show consistent improvements in intra-cluster coherence and inter-cluster dissimilarity, with window-size robustness and case studies illustrating regime detection during events like GameStop and AI valuation corrections. The approach provides a scalable, data-driven pathway for dynamic portfolio construction and risk management by revealing hidden, temporally coherent behavioral communities that transcend static sector classifications.

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.

Paper Structure

This paper contains 58 sections, 70 equations, 11 figures, 3 tables, 4 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Graph Community Detection with Deep Learning
  4. Temporal Graph Learning and Dynamic Community Detection
  5. Methodology
  6. Overview
  7. Problem Statement
  8. Scale-Adaptive Encoding
  9. Dynamic Connectivity Modeling with NAV Edge Embedding
  10. Multi-Stream Fusion
  11. Architecture and Optimization
  12. Experiments
  13. Experimental Setup
  14. Baselines and Metrics
  15. Main Results
  16. ...and 43 more sections

Figures (11)

  • Figure 1: Temporal behavioral divergence exposing failures of snapshot clustering and sector grouping
  • Figure 2: FTSCommDetector architecture: multi-scale temporal encoding, adaptive graph learning, and gated fusion
  • Figure 3: Dynamic attention progression on SP100.
  • Figure 4: GameStop event (Jan-Jun 2021): Market fragmentation into 6 distinct behavioral clusters.
  • Figure 5: Window size analysis on SP500. Sharp IntraCorr decline for short windows indicates volatility noise; steep InterDissim decline for long windows suggests regime mixing.
  • ...and 6 more figures

Theorems & Definitions (4)

  • proof
  • proof
  • proof
  • proof