NationFiles Stability Index (NFSI) – Validation and Verification ReportPublicationsNationFiles NFSI Geopolitical Risk Analysis  Validation Report NationFiles Stability Index (NFSI) – Validation and Verification ReportThis report describes the methodology, data provenance, and technical traceability of the NationFiles Stability Index (NFSI) and the 7-day forecast. It is intended for universities, public authorities, and accredited auditors to verify and endorse the credibility and reproducibility of the system. NationFiles is committed to absolute transparency: all formulas and parameters stated correspond to the actual implementation; no values are invented or adjusted ex post. Evidence SnapshotThis publication documents NationFiles Stability Index (NFSI) – Validation and Verification Report as a Validation Report from 2026. Structural scope: 8 main sections. DOI reference: 10.5281/zenodo.19783682. Chapter 1: System Architecture and Data Pipeline1.1 Integrity and Credibility All NFSI values and forecasts are produced solely from documented formulas and the sources listed in the data inventory. There are no manual overrides or black-box corrections. Every published NFSI value can be reconstructed from the documented formulas, constants, and input data. 1.2 Absolute Transparency NationFiles discloses all formulas and calculation steps (Layers 1–4), including pseudo-code and the complete constants table. Changes to model and data are versioned; an audit trail (timestamps, hashes) ensures traceability. Chapter 2: Layer 1 – Normalization and Direction2.1 Min-Max Normalization Per row: normalized value = (raw − minimum) / span × 100. The result lies in [0, 100]. 2.2 Direction Rule Depending on the indicator: 'higher raw = worse stability' → score = 100 − normalized; otherwise score = normalized. The score is clamped to [0, 100] and rounded to two decimals. Chapter 3: Layer 2 – Thematic Aggregation and Inertia3.1 Security Group (Group 100) For security-critical sources: daily score = minimum of scores (one critical event suffices). Missing values are padded with 100 (conservative). 3.2 Other Groups For all other sources: daily score = arithmetic mean of scores, with fixed dummy values 0 and 100, plus pad value 50 for missing entries. 3.3 Smoothing Formula Final Layer-2 value: 0.6 × daily score + 0.4 × previous day. Missing days are bridged by recovery logic (max. 90 days, cap 95). Chapter 4: Layer 3 – Country NFSI and Adjustments4.1 Weighted Average Effective weight: group × (source weight/100) × update multiplier. The base score is calculated as a weighted average of all connector scores, including fixed dummy values 0 and 100 (weight 1 each). 4.2 Conflict Malus If the minimum of security scores (group 100) falls below 70, a conflict malus is deducted (max. 35 points). This ensures acute security crises have a significant impact on the overall result. 4.3 Fragility and Population Adjustments Fragility malus (governance gap × population sensitivity, max. 15), small-country malus (<5 million inhabitants, max. 25), population bonus (log₁₀(population) × 0.5, max. 4), and WGI pull (governance raises raw score, factor 0.95). Chapter 5: Layer 4 – Daily Inertia Smoothing and Crash Mode5.1 Standard Inertia Standard: 80% previous day, 20% today's raw score. When many connectors have missing data (≥50%): 45% previous, 55% today. Maximum daily change: ±3 points. 5.2 Crash Mode If the minimum of security scores falls below 25, smoothing is suspended: the NFSI value immediately equals the raw score. This enables immediate reaction to acute crises without dampening delay. Chapter 6: 7-Day ForecastThe 7-day forecast is based on a VAR (vector autoregression) approach: it uses the last 90 days of historical data and multiple time series (e.g. world NFSI, country-specific and global news risk). The simulation runs iteratively over 7 days with plausibility bounds and reversion targets. No separate validation metrics (e.g. RMSE) are claimed in this document; the description serves methodological traceability. Chapter 7: Data Source InventoryThe NFSI integrates over 45 verified data sources with defined weights. The most important include:
Chapter 8: Governance, Audit and Change ManagementVersioning: semantic versioning for model and data changes; each release includes changelog and snapshot hashes. Audit trail: immutable logs (hash + timestamp) for data snapshots and model binaries. External audit: onboarding process (NDA → temporary data access → reproducibility run → audit report). Error correction: report to data@nationfiles.com → source review → database correction → automatic recalculation; manual score overrides are excluded. Loading… Loading PDF… Could not load PDF. Open directly Frequently Asked QuestionsWhat is "NationFiles Stability Index (NFSI) – Validation and Verification Report"?This report describes the methodology, data provenance, and technical traceability of the NationFiles Stability Index (NFSI) and the 7-day forecast. It is intended for universities, public authorities, and accredited… Who is the author of "NationFiles Stability Index (NFSI) – Validation and Verification Report"?Sven Schmidt (Sven Neawolf), ORCID: 0009-0002-5010-1902. Founder of Neawolf Media Group and Lead Architect of the Naciro Engine and NationFiles platform. Where is "NationFiles Stability Index (NFSI) – Validation and Verification Report" published?Open-access on Zenodo (DOI: 10.5281/zenodo.19783682). License: Creative Commons CC BY 4.0. How to cite "NationFiles Stability Index (NFSI) – Validation and Verification Report"?Schmidt, Sven (2026). NationFiles Stability Index (NFSI) – Validation and Verification Report. Neawolf Media Group. DOI: https://doi.org/10.5281/zenodo.19783682 What other publications are available from NationFiles?All technical publications are available at: https://nationfiles.com/en/publications/ Project Credits
ReferencesNeawolf Media Group / NationFiles (2026). Validation and Verification Report — NationFiles Stability Index (NFSI). Version 1.0. Aachen. DOI: 10.5281/zenodo.19783682 |