Paper: arXiv 2508.19994

Authors: Noah Shore

Abstract

The Coherent Multiplex is formalized and validated as a scalable, real-time system for identifying, analyzing, and visualizing coherence among multiple time series. Its architecture comprises a fast spectral similarity layer based on cosine similarity metrics of Fourier-transformed signals, and a sparse time-frequency layer for wavelet coherence. The system constructs and evolves a multilayer graph representing inter-signal relationships, enabling low-latency inference and monitoring. A simulation prototype demonstrates functionality across 8 synthetic channels with a high similarity threshold for further computation, with additional opportunities for scaling the architecture up to support thousands of input signals with constrained hardware. Applications discussed include neuroscience, finance, and biomedical signal analysis.

Complexity vs Empirical Score

  • Math Complexity: 4.5/10
  • Empirical Rigor: 3.5/10
  • Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: The paper presents an interesting signal processing and streaming graph architecture combining FFT cosine similarity heuristics with fast continuous wavelet coherence. However, its empirical validation is limited to basic synthetic benchmarking without real-world financial backtesting or multi-asset empirical experiments.

Research Flowchart

  flowchart TD
    A["Research Goal: Real-Time Multi-Signal Coherence"] --> B["Data Inputs: Multi-Channel Time Series"]
    B --> C["Layer 1: Fast Spectral Similarity<br>(FFT & Cosine Metric Filter)"]
    C --> D["Layer 2: Sparse Wavelet Coherence<br>(Time-Frequency Analysis)"]
    D --> E["Graph Construction: Dynamic Multilayer Graph"]
    E --> F["Validation: 8-Channel Prototype Simulation"]
    F --> G["Outcomes: Scalable, Low-Latency Cross-Asset Inference"]

Cited References (24)

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0 of 24 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.