THE RESULT
What the data shows
The largest computed metric is 1.018 for Median baseline; the smallest is 0.4227 for Extra trees. Metric: mae (CO reference-unit MAE).
Origin is one observed hour before target. Lagged reference availability is assumed; production outages would change feasibility.
| model | mae | rmse | r2 | bias |
|---|---|---|---|---|
| Median baseline | 1.018 | 1.349 | -0.006106 | -0.1051 |
| Ridge | 0.4869 | 0.7138 | 0.7184 | 0.03062 |
| Extra trees | 0.4227 | 0.6241 | 0.7847 | 0.02036 |
THE METHOD
From source to answer
Complete-grid lags and a chronological holdout, excluding contemporaneous target-hour observations.
The Python source exposes this study’s transformations. The complete project download includes shared preparation and evaluation routines.
EVALUATION
How the result was checked
First 80% of ordered observations train; final 20% test.
Fixed configurations specified before scoring; no tuning on the holdout.
Target and input features
Target: co_gt
Inputs: hour, co_lag_1, co_lag_24, sensor_lag_1
THE NEXT DECISION
What follows from the finding
Assess latency and later-period performance before testing an operational forecast.
Where the conclusion stops
The -200 sentinel is treated as missing. Reference instruments have incomplete coverage; comparisons use paired observations. Sensor calibration is retrospective, not a health or regulatory compliance assessment.
Related studies may reuse observations or holdouts. These are historical analyses; associations and backtests do not demonstrate commercial impact. Further model tuning needs new, untouched evaluation data.