Temporal CO₂ Analytics (Day/Night & Trends) (C2D)
Purpose of the Algorithm
This algorithm focuses on temporal dynamics in CO₂ measurements, identifying daily cycles, trends, and time‑dependent anomalies that are not visible in static aggregates.
What the Algorithm Does
Day vs night segmentation
- Separates measurements into day and night windows
- Computes mean and median CO₂ for each period
- Calculates day‑night amplitude (Δ)
Temporal stability
- Identifies sensors or regions with abnormal nocturnal behavior
- Detects volatility differences between day and night
Trend estimation
- Identifies increasing or decreasing CO₂ tendencies
- Supports long‑term monitoring use cases
Anomaly enrichment
- Highlights time‑localized spikes
- Detects unusual temporal patterns
Value in a Compute‑to‑Data Workflow
- Adds contextual meaning to raw ppm values
- Enables storytelling and explainable analytics
- Keeps raw timestamps private
Industry Value
Applicable to:
- Environmental impact studies
- Operational monitoring
- Alerting and forecasting pipelines
- KPI‑driven dashboards