voidly
Multi-horizon shutdown forecast

India — three honest horizons

How shutdown risk for India evolves over 1 day, 7 days, and 30 days. Each horizon uses its own XGBoost model with per-horizon SHAP attribution and a 90% conformal interval.

Generated 2026-05-21 12:51 UTC · model multi-horizon-h-v1 · raw JSON

Probabilities

1 day
1d
22.1%
90% interval [12%, 32%]
7 days
7d
58.8%
90% interval [40%, 78%]
30 days
30d
90.5%
90% interval [41%, 100%]

Consistency check

OK — monotonicP(1d) ≤ P(7d) ≤ P(30d)

The three horizons are internally consistent: longer windows capture at least the risk of shorter windows. This is the expected behavior — a 30-day window must contain any 7-day or 1-day shutdown.

22.1% ≤ 58.8% ≤ 90.5%

Top-5 SHAP features per horizon

Each horizon has its own learned drivers. Positive SHAP values push the prediction up; negative values push it down. Features differ across horizons because near-term risk responds to acute signals, while month-out risk responds to slower structural factors.

HorizonRankFeatureSHAP
1-day#1week_of_year-0.859
#2recent_shutdown-0.417
#3high_urgency_signals_7d+0.305
#4risk_tier-0.276
#5day_of_week-0.222
7-day#1week_of_year-0.616
#2risk_tier-0.383
#3block_rate_roll7_std-0.238
#4recent_shutdown-0.225
#5blocked_count_roll7_mean+0.118
30-day#1week_of_year-1.148
#2critical_incident_7d+0.339
#3gdelt_unrest_30d+0.305
#4risk_tier-0.221
#5recent_shutdown-0.188

Honest caveats

  • Each horizon is a separate XGBoost model with its own features and its own honest evaluation. LOCO AUC across spotlight countries: 1d 0.91 · 7d 0.88 · 30d 0.84.
  • 30-day forecasts inherently have wider conformal intervals (more time = more uncertainty). A wide interval is not a bug — it's honesty.
  • These numbers are not guarantees. A 70% probability does not mean a shutdown is certain; a 20% probability does not mean you're safe.
  • See /atlas/multi-horizon for the cross-country index, or /atlas/models for the full model registry.

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