Multi-horizon shutdown forecast

Iran — three honest horizons

How the current-regime shutdown-risk level for Iran 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 2026-06-15 07:36 UTC · model multi-horizon-h-v1 · raw JSON

Probabilities

1 day

22.1%

90% interval [17%, 27%]

7 days

35.6%

90% interval [0%, 86%]

30 days

30d

95.2%

90% interval [45%, 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% ≤ 35.6% ≤ 95.2%

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	-1.080
	#2	day_of_week	-0.253
	#3	block_rate_roll7_mean	-0.165
	#4	block_rate_roll14_mean	+0.152
	#5	incident_count_7d	-0.120
7-day	#1	week_of_year	-1.221
	#2	risk_tier	-0.588
	#3	block_rate_roll7_std	-0.201
	#4	month	-0.148
	#5	block_rate_roll7_mean	+0.090
30-day	#1	week_of_year	-0.872
	#2	block_rate_roll14_mean	+0.474
	#3	block_rate_lag1	+0.270
	#4	gdelt_unrest_30d	+0.240
	#5	block_rate_roll7_std	+0.169

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/ir — 7-day calibrated forecast with drivers
/ir — Iran country profile
/atlas/multi-horizon — all spotlight countries with 30d probability
/v1/forecast/IR/multi-horizon — raw JSON

Probabilities

Consistency check

Top-5 SHAP features per horizon

Honest caveats

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