Multi-horizon shutdown forecast

India — three honest horizons

How the current-regime shutdown-risk level for India reads 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. These are current-regime risk signals, not onset predictions — see the scope note below.

Generated 2026-05-23 20:46 UTC · model multi-horizon-h-v1 · raw JSON

Probabilities

1 day

22.1%

90% interval [12%, 32%]

7 days

58.8%

90% interval [40%, 78%]

30 days

30d

90.5%

90% interval [41%, 100%]

What this forecast is — and is not

The 7-day forecast is a current-regime risk signal: it reliably tells you which countries are currently in a sustained internet-blocking regime. It is not a shutdown-onset predictor — it does not reliably call a new shutdown before it happens.

On an honest forward-temporal split (train on the past, test only on the strictly-future window) the production model scores AUC 0.589 on its raw label — near chance.
The label target_7day is a 7-day sliding window, so it is 98.9% autocorrelated day-to-day. A trivial predict-yesterday baseline scores AUC 0.957 — high numbers here mean “what is blocked stays blocked,” not predictive skill.
On the rows where a shutdown actually begins (transitions) the forecast scores AUC ~0.33 — below chance.

Use it to see where censorship is entrenched today and to read the SHAP drivers + conformal interval — not as an early-warning siren. Read the full honest re-evaluation →

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.

Horizon	Rank	Feature	SHAP
1-day	#1	week_of_year	-0.802
	#2	recent_shutdown	-0.402
	#3	risk_tier	-0.270
	#4	high_urgency_signals_7d	+0.270
	#5	day_of_week	-0.203
7-day	#1	week_of_year	-0.688
	#2	risk_tier	-0.383
	#3	recent_shutdown	-0.225
	#4	block_rate_roll7_std	-0.147
	#5	month	-0.123
30-day	#1	week_of_year	-1.154
	#2	critical_incident_7d	+0.342
	#3	gdelt_unrest_30d	+0.305
	#4	risk_tier	-0.223
	#5	block_rate_lag1	+0.187

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.

/atlas/forecast/in — 7-day calibrated forecast with drivers
/in — India country profile
/atlas/multi-horizon — all spotlight countries with 30d probability
/v1/forecast/IN/multi-horizon — raw JSON

Probabilities

Consistency check

Top-5 SHAP features per horizon

Honest caveats

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