Forecast accuracy
Every hour we store forecasts for 121 Seoul areas and compare them with Seoul's actual values when that hour arrives. ‘Level match’ is the share of correct five-level predictions; ‘mean error’ is the score difference on a 0–100 scale.
Last 7 days
Seoul official forecast · 1 h ahead
68%
Level match · Mean error 8.4pt · Comparisons 19,844
Within one level 95% · Daytime (08–22 h) level match 56%
Baseline · current value carried 1 h ahead
86%
Level match · Mean error 3.7pt · Comparisons 5,324
Within one level 97% · Daytime (08–22 h) level match 79%
CrowdView short-term blend · 1 h ahead (current value + bias-corrected official forecast)
86%
Level match · Mean error 3.5pt · Comparisons 5,324
Within one level 98% · Daytime (08–22 h) level match 78%
Seoul official forecast · 3 h ahead
68%
Level match · Mean error 8.5pt · Comparisons 19,844
Within one level 94% · Daytime (08–22 h) level match 55%
Baseline · current value carried 3 h ahead
80%
Level match · Mean error 5.8pt · Comparisons 5,082
Within one level 94% · Daytime (08–22 h) level match 72%
CrowdView short-term blend · 3 h ahead (current value + bias-corrected official forecast)
80%
Level match · Mean error 5.3pt · Comparisons 5,082
Within one level 96% · Daytime (08–22 h) level match 71%
CrowdView pattern forecast · 24 h ahead
27%
Level match · Mean error 20.4pt · Comparisons 19,352
Within one level 80% · Daytime (08–22 h) level match 31%
Baseline · current value carried 24 h ahead
78%
Level match · Mean error 6.3pt · Comparisons 19,844
Within one level 88% · Daytime (08–22 h) level match 56%
Baseline · median of the same weekday and hour, last 4 weeks
–
Level match · Mean error – · Comparisons 0
Baseline · same weekday and hour last week
89%
Level match · Mean error 2.7pt · Comparisons 2,904
Within one level 99% · Daytime (08–22 h) level match 82%
CrowdView blend · current value if tomorrow is the same kind of day, last week's same hour when weekday↔weekend changes
84%
Level match · Mean error 4.4pt · Comparisons 2,904
Within one level 96% · Daytime (08–22 h) level match 77%
CrowdView day-type median · weekdays: median of recent weekdays at the same hour; days off: same weekday and hour last week
–
Level match · Mean error – · Comparisons 0
CrowdView ensemble · per area, more weight on the baselines that have been right lately
82%
Level match · Mean error 4.5pt · Comparisons 2,541
Within one level 97% · Daytime (08–22 h) level match 71%
CrowdView learned model · corrects itself every hour from actual results
81%
Level match · Mean error 4.5pt · Comparisons 2,541
Within one level 98% · Daytime (08–22 h) level match 71%
Method currently used for 24-hour-ahead forecasts (tomorrow/weekend scores in quiet routes): Baseline · current value carried 24 h ahead. A baseline or blend replaces the pattern forecast only when it beats it on both level match and mean error over the last 7 days (at least 3,000 comparisons).
The learned model is a formula over the current value, last week's same hour (averaged with the hours either side), the 4-week median, whether weekday↔weekend changes and whether it is night. Every hour, when the actual value arrives, it nudges the formula's weights by how wrong it was (slowly forgetting old results) and corrects each area's leftover bias. The ensemble remembers each baseline's recent error per area and leans on whichever has been right. The short-term blend removes each area's usual over/under-estimate from Seoul's official forecast and mixes it with the current value by their recent errors. The day-type median uses, for weekdays, the middle value of the same hour on the weekdays of the past 2–7 days (plus the current value when today is a weekday); for weekends and public holidays, the same weekday and hour last week (the latest Sunday for a weekday holiday). The ensemble, learned model and day-type median are used for tomorrow/weekend forecasts only when they beat every baseline and blend on both level match and mean error.
| Date | Seoul official forecast · 1 h ahead | Baseline · current value carried 1 h ahead | CrowdView short-term blend · 1 h ahead (current value + bias-corrected official forecast) | Seoul official forecast · 3 h ahead | Baseline · current value carried 3 h ahead | CrowdView short-term blend · 3 h ahead (current value + bias-corrected official forecast) | CrowdView pattern forecast · 24 h ahead | Baseline · current value carried 24 h ahead | Baseline · median of the same weekday and hour, last 4 weeks | Baseline · same weekday and hour last week | CrowdView blend · current value if tomorrow is the same kind of day, last week's same hour when weekday↔weekend changes | CrowdView day-type median · weekdays: median of recent weekdays at the same hour; days off: same weekday and hour last week | CrowdView ensemble · per area, more weight on the baselines that have been right lately | CrowdView learned model · corrects itself every hour from actual results |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2026-11-02 | 66% · 9.0pt · 2420 · ±1 93% | 82% · 4.9pt · 2420 · ±1 96% | 81% · 4.7pt · 2420 · ±1 97% | 67% · 8.6pt · 2420 · ±1 93% | 75% · 7.1pt · 2420 · ±1 93% | 76% · 6.3pt · 2420 · ±1 96% | 16% · 30.2pt · 2360 · ±1 68% | 69% · 10.4pt · 2420 · ±1 87% | – | 88% · 2.8pt · 2420 · ±1 99% | 88% · 2.8pt · 2420 · ±1 99% | – | 81% · 4.6pt · 2420 · ±1 97% | 80% · 4.7pt · 2420 · ±1 98% |
| 2026-11-01 | 78% · 5.6pt · 2904 · ±1 97% | 90% · 2.6pt · 2904 · ±1 98% | 90% · 2.5pt · 2904 · ±1 98% | 76% · 6.6pt · 2904 · ±1 95% | 85% · 4.7pt · 2662 · ±1 95% | 85% · 4.3pt · 2662 · ±1 96% | 27% · 19.5pt · 2832 · ±1 90% | 75% · 8.5pt · 2904 · ±1 89% | – | 93% · 2.1pt · 484 · ±1 98% | 67% · 12.5pt · 484 · ±1 83% | – | 92% · 2.6pt · 121 · ±1 97% | 98% · 0.5pt · 121 · ±1 99% |
| 2026-10-31 | 68% · 8.5pt · 2904 | – | – | 68% · 8.6pt · 2904 | – | – | 28% · 18.0pt · 2832 | 67% · 9.7pt · 2904 | – | – | – | – | – | – |
| 2026-10-30 | 64% · 9.5pt · 2904 | – | – | 64% · 9.5pt · 2904 | – | – | 29% · 18.4pt · 2832 | 80% · 4.9pt · 2904 | – | – | – | – | – | – |
| 2026-10-29 | 64% · 9.4pt · 2904 | – | – | 64% · 9.6pt · 2904 | – | – | 31% · 18.0pt · 2832 | 84% · 3.9pt · 2904 | – | – | – | – | – | – |
| 2026-10-28 | 64% · 9.3pt · 2904 | – | – | 65% · 9.3pt · 2904 | – | – | 32% · 18.7pt · 2832 | 83% · 4.4pt · 2904 | – | – | – | – | – | – |
| 2026-10-27 | 71% · 7.6pt · 2904 | – | – | 71% · 7.8pt · 2904 | – | – | 27% · 21.3pt · 2832 | 86% · 3.3pt · 2904 | – | – | – | – | – | – |
| 2026-10-26 | 65% · 9.0pt · 2904 | – | – | 64% · 9.7pt · 2904 | – | – | 17% · 30.7pt · 2832 | 72% · 8.5pt · 2904 | – | – | – | – | – | – |
| 2026-10-25 | 66% · 9.2pt · 2904 | – | – | 66% · 9.3pt · 2904 | – | – | 28% · 18.2pt · 2832 | 76% · 6.4pt · 2904 | – | – | – | – | – | – |
| 2026-10-24 | 60% · 10.7pt · 2890 | – | – | 61% · 10.8pt · 2892 | – | – | 25% · 18.9pt · 2832 | 69% · 8.6pt · 2904 | – | – | – | – | – | – |
| 2026-10-23 | 58% · 11.5pt · 2890 | – | – | 59% · 11.2pt · 2898 | – | – | 29% · 18.6pt · 2832 | 79% · 5.7pt · 2904 | – | – | – | – | – | – |
| 2026-10-22 | 62% · 10.0pt · 2876 | – | – | 64% · 9.9pt · 2892 | – | – | 29% · 18.9pt · 2832 | 79% · 5.5pt · 605 | – | – | – | – | – | – |
| 2026-10-21 | 61% · 11.0pt · 2880 | – | – | 61% · 10.9pt · 2898 | – | – | 29% · 19.9pt · 2832 | – | – | – | – | – | – | – |
| 2026-10-20 | 63% · 10.0pt · 2904 | – | – | 62% · 10.4pt · 2904 | – | – | 29% · 20.7pt · 2829 | – | – | – | – | – | – | – |
CrowdView model vs. measured, by place (last 7 days)
Where live measurements exist (Seoul areas, theme park waits, airports), every hour we compare what the model alone would have said with the actual value, learn the gap by weekday/weekend × hour, and correct the forecast. Places of the same kind without live data get half of that correction.
| Place | Error before | Error after | Model bias | Comparisons |
|---|---|---|---|---|
| 爱宝乐园 | 18.0pt | 14.7pt | -9.5pt | 87 |
| 仁川机场 T1航站楼 | 18.9pt | 18.9pt | -9.1pt | 165 |
| 仁川机场 T2航站楼 | 18.1pt | 18.1pt | -11.2pt | 165 |
| 金浦机场 国内航线 | 15.5pt | 15.5pt | -6.8pt | 79 |
| 济州国际机场 | 13.6pt | 13.6pt | -4.1pt | 81 |
| 金海机场 国内航线 | 15.6pt | 15.6pt | +15.0pt | 80 |
| 首尔世界烟花节 | 11.2pt | 9.1pt | +10.8pt | 69 |
| 韩国乐高乐园 | 25.9pt | 20.2pt | -12.9pt | 54 |
| 加勒比海湾水上乐园 | 14.9pt | 11.0pt | -13.2pt | 36 |