ghostdrift-adic-audit

Certificate-Based Drift Detection Audit for Time-Series Forecasting (Electricity Demand × Weather)

Keywords: drift detection, time-series forecasting, model monitoring, MLOps, audit trail, reproducibility, accountability, electricity demand forecasting

Certificate–Ledger–Verifier Flow

Protocol overview: fixed certificate → append-only ledger → independent verifier (OK / NG).

ghost-drift-audit is a certificate-based audit engine for drift detection in operational time-series forecasting (MLOps). It outputs a verifiable certificate + immutable ledger so any third party can reproduce the same OK/NG verdict from the same inputs—no post-hoc threshold tuning after results are observed. Note: Bundled CSVs are dummy data for smoke tests. The published certificates and audit report correspond to the real Jan–Apr 2024 electricity demand × weather dataset. Case study: electricity demand forecasting (power demand × weather, Jan–Apr 2024). This repo exports certificates, ledgers, and evidence time series as reproducible audit artifacts.

🔗 Quick Links

📂 Source Code: GitHub Repository
📜 Main Script: ghost_drift_audit_EN.py
📦 Download: Project ZIP
📖 Documentation: Online Manual (⚙️ Jump to Execution Mode)
🚨 Support: Report Issues

📑 Audit Report (PDF)

Report: Scientific Audit Report on Structural Integrity of Forecasting Models
Verdict: NG (TAU_CAP_HIT)
Protocol: Ghost Drift Audit v8.0

💎 Design Philosophy: From “Probabilistic” to “Accountable”

To address the “opaque inference” problem in conventional AI operations, this framework provides the following.

[!TIP] Audit-First Design Alongside running predictions, it automatically generates objectively verifiable evidence for third parties.

[!IMPORTANT] Tamper-evident Fingerprints It fixes hash fingerprints of input data and configuration parameters, making post-hoc modifications mathematically detectable.

[!NOTE] Verifiable Integrity Rather than mere statistical optimality, it makes visible the model’s faithful adherence to operational rules.

🛠 Technical Specifications

System Requirements

Language: Python 3.10+
Dependencies: numpy, pandas, matplotlib, lightgbm

Project Structure

.
├── ghost_drift_audit_JP.py    # Core Logic & Audit Engine
├── electric_load_weather.csv  # Input: Weather (dummy for smoke test)
├── power_usage.csv            # Input: Demand (dummy for smoke test)
└── adic_out/                  # Output: Accountability Ledger

⚙️ Execution Profiles

Switch the strictness of the audit via AUDIT_CONFIG['PROFILE'].

Profile	Use / Target	Strictness	Key Features
demo	Smoke test / learning	Low	Prioritizes understanding behavior and evidence output
paper	Research / reproducible experiments	Mid	Ensures computational reproducibility via fixed seeds
commercial	Production / decision-making	High	Produces strict gate checks and a final verdict

How to Configure

AUDIT_CONFIG = {
  "PROFILE": "demo",  # "demo" | "paper" | "commercial"
}

🚀 Deployment & Usage

1. Setup

pip install numpy pandas matplotlib lightgbm

2. Prepare Data

Place the CSV files in the same directory as the .py.

[!CAUTION] The bundled CSVs are synthetic (dummy) data. They are for smoke testing only; for production use or research, use real data for which you hold the rights.

3. Run

python ghost_drift_audit_JP.py

4. Verification (adic_out/)

📜 certificate.json: a “certificate” of execution conditions and verdict summary
📑 ledger.csv: an immutable “ledger” that records the full history
📉 evidence_timeseries.csv: time-series evidence for re-verification

⚖️ Scope & Integrity (Non-claims)

🎯 Scope & Limits

Scope: Make the model’s behavior and assumption failures observable, and fix an audit trail for intervention decisions.
Non-claims: Does not guarantee zero future error, a mathematically “unique correct answer,” or generalization in extrapolation regimes.

🛡️ Threat Model (Tamper Detection)

Threshold manipulation: Arbitrary changes to conceal anomalies → detected via Cap records
Baseline manipulation: Post-hoc changes to comparison baselines → detected via config fingerprint mismatches
Data fabrication: Input swapping or fabrication → verified against Data Fingerprints

📜 License & Acknowledgments

Research and evaluation use: permitted without charge
Commercial use: requires explicit license from GhostDrift Mathematical Institute
All other uses: prohibited without permission

From “prediction” to “accountability.” This repository provides a practical reference implementation for certificate-based drift detection, audit trails, and accountable model monitoring in time-series forecasting systems. Produced by GhostDrift Mathematical Institute (GMI) — Official Website | Online Documentation