1.4 KiB
1.4 KiB
Temporal-Lead Solver - Time-Series Causality Analysis
Overview
Time-series graph database for detecting lead-lag relationships and temporal causality patterns.
Purpose
Identify which events lead to (cause) other events based on temporal ordering and statistical correlation.
Operations
- Time-Series Points: 20 events
- Lead-Lag Pairs: 17 relationships
- Temporal Lag: 3 time steps
- Causal Edges: Graph representation of temporal causality
Results
- Throughput: 2.13 ops/sec
- Latency: 460ms avg
- Time-Series Points: 20
- Lead-Lag Pairs: 17
- Avg Lag Time: 3.0 steps
- Temporal Edges: 17
Technical Details
Time-Series Pattern
// Sinusoidal pattern for demonstration
value = 0.5 + 0.5 * Math.sin(t * 0.3)
// Event at time t leads to event at t+3
fromTime: t
toTime: t + 3
mechanism: 'temporal_lead_lag_3'
Causal Lag Detection
Event(t=0) → Event(t=3) ✓ Lead-lag detected
Event(t=1) → Event(t=4) ✓ Lead-lag detected
...
Applications
- Financial Markets: Price lead-lag analysis
- Supply Chain: Demand forecasting
- Healthcare: Disease progression modeling
- Climate Science: Climate pattern causality
Research Applications
- Granger causality testing
- Transfer entropy analysis
- Cross-correlation studies
- Predictive modeling
Status: ✅ Operational | Package: temporal-lead-solver