Indonesia Tourism Revenues

Indonesia · Quarterly series · EC-ID-TR

Latest, Q2 2026

4.39B USD

+8.4% from Q1 2026

Open interactive chartIndonesia Tourism Revenues in the app

6.565B3.327B89.55MQ1 2010Q2 2027
History through Q2 2026, the forecast path to Q2 2027 (dashed) and its 80% interval (shaded).

Forecast

Forecast for Q2 2027: 4.51B USD, with an 80% interval of 3.70B USD to 5.09B USD.

Forecast path. Intervals are shown where available.
PeriodForecast80% low80% high
Q3 20265.45B USD3.99B USD6.56B USD
Q4 20264.58B USD4.11B USD5.00B USD
Q1 20274.23B USD3.85B USD4.55B USD
Q2 20274.51B USD3.70B USD5.09B USD

Model: Agentic Investor TSFM (foundation model). Forecast generated 2026-11-15.

AI confidence

Supported evidence

The point forecast beat the no-change baseline overall and in a majority of the evaluated tests.

Model: Agentic Investor TSFM (foundation model)

Accuracy vs naive
+17%Beats the no-change baseline over 8 tests (8 tests, 6 won)
Direction
84%No clear edge over a coin flip (27 of 32 calls right in 8 tests)
Turning points
7 of 11Anticipated most turns (11 turns in 8 tests, 0 without a turn)
Error vs baseline
17% lowerError ratio 0.83; 1.00 is a tie.
Tests won
6 / 8Complete rolling backtests; ties count as no win.
Test period
2018-09-01 to 2026-06-014 observations ahead per test.
80% band coverage
84%Observed outcomes inside the band across all stored test windows.
How this assessment is made

AI confidence describes historical evidence for this forecast, not a probability of being correct. Each backtest fits on data available before its test window. The baseline carries the last value forward. Supported evidence requires at least six complete, non-overlapping tests, an error ratio below 0.90 (rounded to two decimals), and wins in a majority of tests. Ratios from 0.90 to below 1.10 are on par; 1.10 or above is weak evidence. Inconsistent wins produce mixed evidence. For a selected model, only the recent tests excluded from model selection determine this label. These are historical simulations using the available data, not a live trading track record; revisions and foundation-model pretraining can limit how closely they reproduce a past decision. Past performance can change.

Replay past tests

Model comparison

Comparison pendingA new ticker first receives its serving model's forecast and evaluation. An eligible nightly batch can compare both candidates, subject to call budgets and retry limits.

Reading

It is above 82% of the 39 earlier readings since Q3 2016.

Written from the figures by a fixed template; no AI model is involved.

Recent history (24 observations)
The 24 most recent observations, newest first.
PeriodValue
Q2 20264.39B USD
Q1 20264.05B USD
Q4 20254.45B USD
Q3 20255.62B USD
Q2 20254.39B USD
Q1 20253.81B USD
Q4 20244.07B USD
Q3 20245.16B USD
Q2 20243.83B USD
Q1 20243.63B USD
Q4 20233.53B USD
Q3 20234.38B USD
Q2 20233.25B USD
Q1 20232.83B USD
Q4 20222.52B USD
Q3 20222.39B USD
Q2 20221.60B USD
Q1 2022208.5M USD
Q4 2021187.8M USD
Q3 202193.3M USD
Q2 2021144.9M USD
Q1 202194.7M USD
Q4 2020188.9M USD
Q3 2020112.4M USD

Source

Data: public market data feeds.

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