Custom forecast horizon + previous changes - #4
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Walk-forward evaluation: for each historical hourly point, predicts using
only data strictly before that timestamp (no lookahead), then compares
against actual occupancy.
Usage:
python -m parktrack_ml.evaluate # last 30 days, all zones
python -m parktrack_ml.evaluate --days 60
python -m parktrack_ml.evaluate --days 30 --zone-id 3
Report includes: overall accuracy + MAE, per-class precision/recall/F1,
per-zone breakdown, per-hour-of-day accuracy (catches static-forecast
regression), and a text confusion matrix.
Generates 6 PNG charts from backtest results: - confusion_matrix.png - accuracy_by_hour.png (catches static-forecast regression) - per_zone_metrics.png - class_metrics.png (F1 per class) - predicted_vs_actual.png - feature_importance.png Usage: python -m parktrack_ml.visualize --days 30 --out ./charts
Multi-day historical averages flatten out the real daily pattern — e.g. for a zone that is 0% at night and 70% during the day, _hist_avg() returns ~30% for all hours, making every forecast slot look identical. Fix: when a lag timestamp falls in the future (no real data yet), first try the same timestamp shifted back 24h (yesterday at the same hour). Yesterday's 22:00 was 0% → today's 22:00 forecast gets lag_1h=0.00, not 0.30. Also deduplicate history index before lookups to prevent Series→float errors.
Default changed from 24h to 48h (96 slots per run). Override via env: FORECAST_HOURS=24
nawinds
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Jul 15, 2026
Custom forecast horizon + previous changes
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