🤖 Predictive Gas Model (AI-Based)

[mijinummi]

📌 Overview

Accurately forecasting gas prices allows developers and automated systems to plan transactions, reduce costs, and avoid overpayment. Current real-time estimation is reactive, lacking forward-looking insight.

This task introduces a Predictive Gas Model using historical gas data and regression-based techniques to forecast gas prices for the next 1–3 hours.

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🎯 Objective

Build a predictive analytics module that:

• Leverages historical gas datasets
• Implements regression or ML-based prediction models
• Generates short-term gas price forecasts (1–3 hours ahead)
• Provides structured predictions for integration with wallets, dApps, and automated strategies

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🛠 Scope of Work

1️⃣ Historical Gas Data Preparation

• Aggregate historical gas metrics:
  • Base fee (EIP-1559)
  • Priority fee
  • Gas used per block
  • Network congestion indicators
• Normalize and clean datasets:
  • Handle missing data
  • Convert timestamps to consistent intervals (1-min, 5-min)
• Store in time-series database for training and evaluation

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2️⃣ Predictive Model Implementation

• Choose modeling approach:
  • Linear regression / polynomial regression
  • ARIMA / SARIMA (time-series)
  • Optional: Lightweight ML model (XGBoost, LSTM)
• Features may include:
  • Base fee trends
  • Priority fee trends
  • Mempool congestion metrics
  • Block interval variance
• Train model on historical dataset
• Validate using:
  • Mean Absolute Error (MAE)
  • Root Mean Square Error (RMSE)

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3️⃣ Forecast Generation

• Predict gas prices for 1, 2, 3 hours ahead
• Include prediction confidence intervals
• Support multiple chains (EVM-compatible)
• Output structured prediction format:

{
  "chainId": 1,
  "predictions": [
    { "time": "2026-02-18T10:00:00Z", "predictedBaseFee": "38 gwei", "confidence": 0.9 },
    { "time": "2026-02-18T11:00:00Z", "predictedBaseFee": "42 gwei", "confidence": 0.85 },
    { "time": "2026-02-18T12:00:00Z", "predictedBaseFee": "41 gwei", "confidence": 0.88 }
  ]
}


API Exposure
GET /analytics/predictive-gas?chain=<chainId>&hours=<1-3>

ML / Prediction
Python (optional) with scikit-learn / statsmodels
TensorFlow.js (optional, for JS-based prediction)