Neuro-Symbolic AI Forecasting: The Symbolic Seam

What It Does

Combines neural forecasting (LSTM) with Knowledge Graph constraints via Quadratic Programming. Ensures predictions respect real-world limits — even during crises.

Key Results

Condition	Neural Baseline	Symbolic Seam
Normal markets	RMSE 3.698	RMSE 3.695 (identical)
Black Swan crisis	95.33% violations	6.00% violations ✅
Violations prevented	—	938 / 1,050

How It Works

System 1 (LSTM): Predicts prices from historical data
System 2 (KG): Defines operational bounds (min/max price, daily change limit)
Symbolic Seam: Projects forecast into constraint space using QP solver

Quick Start

pip install yfinance cvxpy torch pandas scikit-learn -q
jupyter notebook Neuro_Symbolic_Forecasting_Kaggle.ipynb

Dataset

Source: Yahoo Finance (yfinance)
Stocks: 50 S&P 500 stocks
Period: Jan 2020 – Jan 2024
Sequences: 48,800 (30-day windows)

Model

LSTM: 64 hidden, 2 layers
Training: 15 epochs, batch 64
Best val loss: 0.000539

Key Findings

Normal: Zero accuracy penalty (3.698 → 3.695 RMSE)
Crisis: 89.33pp improvement in constraint compliance (95.33% → 6.00% violations)
Per-stock: High-volatility stocks improve most (CRM +5.2%, DHR +1.7%)

Why This Matters

Neural models fail when rules change (tariffs, limits, regulations). Symbolic Seam enforces hard constraints at inference time without retraining.

Files

Neuro_Symbolic_Forecasting_Kaggle.ipynb — Single notebook, all stages
IEEE_Symbolic_Seam_Full.docx — Full research paper
models_backup_50stock.zip — Trained model + config

Author

Sanaullah Tareen | PAF-IAST | @SanaullahTareen

Full details in IEEE paper or Kaggle notebook.