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.

Formal Verification

The core lemma of the ADIC framework has been formally verified using the Lean theorem prover.

Lean proof artifact: https://ghostdrifttheory.github.io/adic-lean-proof/

Core file: ADIC_RSound.lean

🔗 Quick Links

📑 Audit Report (PDF)

💎 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

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/)

⚖️ Scope & Integrity (Non-claims)

🎯 Scope & Limits

🛡️ Threat Model (Tamper Detection)

📜 License & Acknowledgments

© 2026 GhostDrift Mathematical Institute.
This version is released under a custom license:

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