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README.md

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+---
+license: mit
+tags:
+- event-prediction
+- temporal-point-process
+- supply-chain
+- universal-event-grammar
+datasets:
+- custom
+language:
+- en
+pipeline_tag: other
+---
+
+# DVCE Event Grammar Model
+
+**Universal Event Grammar Model** — predicts the next event in any sequential system.
+
+Trained on 1.3M+ real-world events across 30+ domains.
+
+## What it does
+
+Given a sequence of past events, predicts:
+1. **What** happens next (291 event types)
+2. **When** it happens (inter-event time)
+3. **How severe** (0-1 severity score)
+
+## Domains trained on
+
+Geopolitics (GDELT), earthquakes (USGS), sports (StatsBomb), commodities, 
+cybersecurity, weather, clinical trials, logistics, financial markets, 
+manufacturing, healthcare, e-commerce, energy grid, IT incidents, 
+DeFi/blockchain, agriculture, construction, and more.
+
+## Architecture
+
+- Transformer Decoder (GPT-style)
+- d_model=256, n_layers=4, n_heads=8
+- 4.5M parameters
+- Continuous time encoding
+- Multi-task output (type + time + severity)
+
+## Usage
+
+
+
+## Training
+
+- 1.3M events from 30+ domains
+- 100 epochs on balanced dataset with domain tokens
+- Trained on AWS SageMaker (g5.xlarge)
+- Total training cost: ~
+
+## License
+
+MIT