WatchActive

Disruption index anomaly in Portugal

🇵🇹 Portugal

Onset: 2026-10 · First detected: 2026-10-12T12:19:22.151444+00:00 · Last observed: 2026-10-12T12:19:22.151444+00:00

Generated at: 2026-10-12T12:19:22.151444+00:00 · The radar runs every 6 hours; data may lag by up to 6 hours.

Plain-language brief

What happened, who is affected, what similar past episodes looked like and what to do — every number links to its source.

What happened

ObservedIn 2026-10, the combined disruption picture for Portugal moved well outside its usual range — rated watch — the lowest of our three alert levels.[Composite Disruption Index (monthly methodology)]
ObservedThe reading behind this alert: index at 30.1 (the full technical detail is in the sections below).[Composite Disruption Index (monthly methodology)]

Who is affected

ObservedHotels, travel managers, OTAs and tour operators with guests in Portugal during the event window are the most directly affected, together with travellers holding bookings there.[Composite Disruption Index (monthly methodology)]

What similar past episodes looked like

Historical associationIn similar past high-disruption months, overnight stays in Portugal averaged 39.5% below normal calm-month levels — a historical association from our published research panel, not a prediction.[estimates.historical_correlation]
Historical associationLooking at the 3 most similar months in our 2014-2026 research panel, overnight stays one month later differed from a year earlier by a median of -0.2% — again a historical association, not a prediction of this event.[estimates.analogues]

What to do now

RecommendationFor now, keep an eye on the situation in Portugal; no change to bookings is needed yet.[detection.observed]

Impact

index	30.12
components	{"aviation":0,"disaster":28.87,"health":0,"rail":null,"weather":37.5}

Historical monthly context — not current conditionsAs of: 2026-10 · Disruption index: 30.12 · 0 months older than this event

How this was detected

Rule: monthly_index_anomaly

Method: deterministic-rule

Thresholds

index_watch	25
index_severe	50
z_min	2
z_floor_index	10

Observed

month	2026-10
disruption_index	30.12
components	{"aviation":0,"disaster":28.87,"health":0,"rail":null,"weather":37.5}

Baseline

n_points	140
window_hours	—
mean	33.60
std	21.84

Insufficient baseline — absolute threshold only.

Sources

Composite Disruption Index (monthly methodology)snapshotcollected: 2026-10-12

Corroborating evidence

Corroboration not applicable: live snapshots describe current conditions, not the period of this event.

Estimates

Estimates are labeled model outputs derived from published historical artifacts — they are context, not forecasts.

Model output — historical correlation estimatehistorical-correlation

r_nights	-0.09
r_arrivals	-0.13
best_lag_months	3
best_lag_r	-0.40
baseline_deviation_pct	-39.47
n_months	139

Caveats:

correlation is association, not causation
baseline_deviation_pct is the observed average nights deviation during historical high-disruption months (index > 50) vs calm months
coefficients are dominated by the 2020-2021 pandemic period and may overstate the impact of smaller disruptions
computed on monthly national aggregates; sub-national and short-lived events are diluted

Observed history — analogue episodes, not a predictionanalogue-episodesindex_equiv = 25

Episode	score	index	h0	h1	h2	h3
🇫🇷 France · 2026-07	1.524	93.33	2.97	—	—	—
🇫🇷 France · 2016-07	1.466	81.18	-1.78	-1.98	-1.28	-9.14
🇫🇷 France · 2024-07	1.425	84.98	-3.32	1.48	-4.88	15.42

median_h1	-0.25
median_h2	-3.08
median_h3	3.14

Caveats:

analogues are historically similar months, not a forecast of this event
similarity is computed on a 10-feature panel embedding (tourism volumes, disruption/stringency indices, seasonality); it ignores event semantics
the index_equiv mapping from severity to disruption-index points is a disclosed modelling assumption, not an observation
observed_nights_yoy_pct mixes the event effect with everything else that happened in those months

Recommendations

Monitor the situation in Portugal and re-check this report before confirming new bookings.
based on: detection.observed

For AI agents

Machine-readable JSON for this page:

/data/api/v1/events/ev-monthly-anomaly-pt-20261001-2c57a1.json

Add as an MCP server (from the repo root):

claude mcp add travel-trends -- .venv/bin/python -m etl.mcp_server

API documentation→