Multi-horizon forecast index
Three honest forecast horizons per country — each with its own XGBoost model, its own SHAP attribution, and its own 90% conformal interval. Click a country for per-horizon detail.
Trained 2026-05-21 · Recommendation: ship_all_three · model info
20 spotlight countries · ranked by 30-day risk
| # | Country | 1d | 7d | 30d | 30d range (90%) | Cons. |
|---|---|---|---|---|---|---|
| 1 | Iran IR | 22% | 36% | 95% | [45, 100] | ok |
| 2 | China CN | 22% | 59% | 95% | [45, 100] | ok |
| 3 | Azerbaijan AZ | 15% | 59% | 91% | [41, 100] | ok |
| 4 | Kazakhstan KZ | 15% | 59% | 91% | [41, 100] | ok |
| 5 | Bangladesh BD | 15% | 59% | 91% | [41, 100] | ok |
| 6 | India IN | 15% | 59% | 91% | [41, 100] | ok |
| 7 | Egypt EG | 15% | 59% | 83% | [33, 100] | ok |
| 8 | Venezuela VE | 15% | 59% | 83% | [33, 100] | ok |
| 9 | Myanmar MM | 22% | 59% | 83% | [42, 100] | ok |
| 10 | Pakistan PK | 15% | 59% | 83% | [33, 100] | ok |
| 11 | Turkey TR | 15% | 59% | 83% | [33, 100] | ok |
| 12 | Russia RU | 22% | 36% | 70% | [20, 100] | ok |
| 13 | Belarus BY | 15% | 59% | 70% | [29, 100] | ok |
| 14 | Cuba CU | 15% | 36% | 70% | [20, 100] | ok |
| 15 | Ethiopia ET | 15% | 59% | 70% | [20, 100] | ok |
| 16 | Turkmenistan TM | 15% | 36% | 70% | [20, 100] | ok |
| 17 | TJ TJ | 15% | 59% | 70% | [20, 100] | ok |
| 18 | Uzbekistan UZ | 15% | 59% | 70% | [28, 100] | ok |
| 19 | AF AF | 15% | 36% | 70% | [20, 100] | ok |
| 20 | Saudi Arabia SA | 15% | 59% | 70% | [20, 100] | ok |
Cons. = monotonicity (P(1d) ≤ P(7d) ≤ P(30d)). 30d range = 90% conformal interval.
Why three horizons?
A single 7-day number is what most journalists ask for, but it hides two important things: the current-regime risk level tomorrow, and the operational ceiling over the next month. We train three independent XGBoost models and publish all three with LOCO AUC numbers and per-horizon SHAP attribution. Note: each horizon uses the same target_Nday sliding-window label as the production v1 forecast, so the LOCO AUCs are inflated by label autocorrelation the same way — these are current-regime risk signals, not shutdown-onset predictors. See the onset-skill finding.
The monotonicity check is a free honesty signal: longer windows must contain shorter windows, so if P(1d) > P(30d) the three models are disagreeing and you should treat the headline numbers with caution. See /atlas/models for the full registry, or /methodology for the full pipeline.
Related
- /atlas/forecast — calibrated 7-day forecast index
- /atlas/score-v2 — level-aware composite score (uses 30d avg)
- /atlas/changelog — model registry history
- /sentinel/calibration — live model-honesty dashboard