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:
- What happens next (291 event types)
- When it happens (inter-event time)
- 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
