THE RESULT
What the data shows
The largest computed metric is 0.07527 for 2011-01; the smallest is 0 for 2011-05. Metric: missing_share (Missing-hour share).
Observed records remain distinct from the complete calendar; absent counts are never filled with zero.
| month | calendar hours | missing hours | missing share |
|---|---|---|---|
| 2011-01 | 744 | 56 | 0.07527 |
| 2011-02 | 672 | 23 | 0.03423 |
| 2011-03 | 744 | 14 | 0.01882 |
| 2011-04 | 720 | 1 | 0.001389 |
| 2011-05 | 744 | 0 | 0 |
| 2011-06 | 720 | 0 | 0 |
| 2011-07 | 744 | 0 | 0 |
| 2011-08 | 744 | 13 | 0.01747 |
| 2011-09 | 720 | 3 | 0.004167 |
| 2011-10 | 744 | 1 | 0.001344 |
| 2011-11 | 720 | 1 | 0.001389 |
| 2011-12 | 744 | 3 | 0.004032 |
THE METHOD
From source to answer
Reindex to a complete hourly timeline and audit monthly observation coverage.
The Python source exposes this study’s transformations. The complete project download includes shared preparation and evaluation routines.
THE NEXT DECISION
What follows from the finding
Clarify whether gaps reflect outages, suppressed zero counts or missing ingestion before training.
Where the conclusion stops
Historical system rentals measure realized use, not unmet demand or station inventory. Missing hours are unknown. Weather associations do not establish causal effects.
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.