CNT Cross-Domain Echo Window v1 — Echo Lags Across Finance, Crypto, Volatility, Seismicity, and Climate

This dataset contains the first “Cross-Domain Echo Window” (CDEW) analysis from Cognitive Nexus Theory (CNT). It quantifies how hazard in one system (equity index, crypto, volatility, seismicity, climate) predicts hazard in another at different time lags HHH. The release includes: Domain metadata tables (cdew_domain_meta*.csv) Full H-sweep AUC tables for real vs surrogate models A summary of statistically supported echo windows (cdew_echo_window_summary.csv) Plots of AUC vs lag and real-vs-surrogate performance for BTCUSD, SPY, and VIX This forms a backbone dataset for CNT’s claim that global systems share structured lead–lag “echo windows” that can be tested, falsified, and extended by other groups. If you want a richer, longer description, you can layer this under that: ConceptThe Cross-Domain Echo Window (CDEW) framework asks: if hazard rises in domain A at time ttt, does that carry predictive information about hazard or drift in domain B at time t+Ht+Ht+H? CDEW v1 applies this to: Finance (SPY) Crypto (BTCUSD) Volatility (VIX) Seismicity (event-based windows) Climate/global drift For each source–target pair and lag HHH, the pipeline estimates predictive performance (e.g. AUC) and compares it against surrogate controls. The result is a map of which domains tend to lead and which tend to follow, and on what timescales. Files in this release cdew_domain_meta.csv, cdew_domain_meta_reload.csv Describes each domain, source files, and how CDEW reconstructs the input time series. cdew_hsweep_auc_real.csv AUC values for real data across domain pairs and H. cdew_hsweep_auc_surrogates.csv, cdew_hsweep_auc_surrogate_summary.csv Surrogate AUCs and summary stats (mean, quantiles) for significance-style comparisons. cdew_echo_window_summary.csv Condensed list of echo windows that meet predefined criteria (e.g., real AUC > surrogate band, minimum effect). Plots in plots/ cdew_auc_vs_H_* show AUC vs H for BTCUSD / SPY / VIX. cdew_real_vs_surrogate_* show real vs surrogate distributions for key domains. CDEW_CDNN_SUMMARY.md Human-readable summary of pipeline and high-level findings. CDEW3_ZENODO_README.md Zenodo-focused README with practical details and usage notes. Role in CNT Provides a testable backbone for CNT’s cross-domain coupling claims. Sits alongside the CNT Global Drift Field v1 as a structural dataset: D_global = “how much is everything drifting?” CDEW = “who nudges whom, and how far ahead?” Future CNT tools (Forbidden Drift Zone, Decoders, Oracle-style early warning) can point back to CDEW as the underlying empirical echo map. Intended use Re-analysis of cross-domain lead–lag patterns. Early-warning / systemic-risk model benchmarking. Testing persistence of echo windows out-of-sample. Independent attempts to falsify or refine CNT’s cross-domain universality claims.