hmnshudhmn24/chronomind-omega

ChronoMind-Ω

ChronoMind-Ω is a Level-5 causal time-series forecasting engine designed to perform multi-horizon predictions with explainable trend attribution and uncertainty estimation.

It fuses temporal transformer modeling, causal inference, regime change detection, and confidence interval modeling into a single, research-grade forecasting system.

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🚀 Key Capabilities

• 🔮 Multi-Horizon Forecasting
• 🧠 Temporal Transformers
• 🔗 Causal Inference
• 🔄 Regime Change Detection
• 📊 Uncertainty & Confidence Intervals
• 🧩 Explainable Trend Attribution
• 🤗 Hugging Face–Ready (time-series-forecasting)

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🧠 System Architecture

Raw Time-Series
   ↓
Data Loader & Normalization
   ↓
Temporal Transformer Encoder
   ↓
Causal Inference Module
   ↓
Regime Change Detector
   ↓
Multi-Horizon Forecaster
   ↓
Uncertainty & Confidence Intervals
   ↓
Trend Attribution Explainer

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📥 Input Format

{
  "series": [0.1, 0.2, 0.35, 0.3, 0.45, 0.6],
  "horizons": [1, 6, 12],
  "causal_features": {
    "holiday_effect": [0, 0, 1, 0, 0, 1]
  },
  "confidence_levels": [0.8, 0.95]
}

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📤 Output Format

{
  "forecasts": {
    "1": 0.63,
    "6": 0.79,
    "12": 0.95
  },
  "confidence_intervals": {
    "1": {
      "0.8": [0.58, 0.68],
      "0.95": [0.54, 0.72]
    }
  },
  "causal_effect": 0.12,
  "regimes": [0, 0, 1, 0, 1],
  "trend": "upward"
}

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🛠️ Installation & Usage

git clone https://huggingface.co/<your-username>/chronomind-omega
cd chronomind-omega
python inference.py

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📁 Project Structure

chronomind-omega/
├── configs/
├── data/
├── src/
├── inference.py
├── evaluation.py
├── README.md
├── model_card.md
├── LICENSE
└── requirements.txt

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🎯 Use Cases

• Financial market forecasting
• Demand & sales prediction
• Energy & climate modeling
• Supply chain forecasting
• Causal time-series analysis

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🔮 Future Improvements

• Full transformer encoder
• Bayesian uncertainty modeling
• Multivariate forecasting
• Interactive dashboard

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📜 License

Apache License 2.0