# The Coherent Multiplex: Scalable Real-Time Wavelet Coherence Architecture

## TL;DR

The paper introduces a scalable, real-time system for analyzing coherence among multiple time series using wavelet and spectral methods, with applications in neuroscience, finance, and biomedical signals.

## Contribution

It presents a novel architecture combining spectral similarity and wavelet coherence layers for low-latency, scalable multi-signal analysis.

## Key findings

- Successfully analyzed 8 synthetic channels in real-time
- Demonstrated potential to scale to thousands of signals
- Validated system performance in simulated environments

## 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.