Japan GDP YoY

Japan · Quarterly series · EC-JP-GDPYY

Latest, Q2 2026

0.9%

+0.30pp from Q1 2026

Open interactive chartJapan GDP YoY in the app

History through Q2 2026, the forecast path to Q2 2027 (dashed) and its 80% interval (shaded).

Forecast

Forecast for Q2 2027: 0.9%, with an 80% interval of -7.8% to 9.5%.

Forecast path. Intervals are shown where available.
Period	Forecast	80% low	80% high
Q3 2026	0.9%	-4.8%	6.5%
Q4 2026	0.9%	-5.2%	7.0%
Q1 2027	0.9%	-7.1%	8.9%
Q2 2027	0.9%	-7.8%	9.5%

Model: Exponential smoothing (statistical). Forecast generated 2026-11-14.

AI confidence

Mixed evidence

The tests show no consistent, material advantage over the no-change baseline.

Model: Exponential smoothing (statistical)

Accuracy vs naive: 0%Matches the no-change baseline over 6 tests (6 tests, 5 won)
Direction: 79%No clear edge over a coin flip (19 of 24 calls right in 6 tests)
Turning points: n/aToo few tests (6 turns, 8 needed) (6 turns in 6 tests, 2 without a turn)

Error vs baseline: About the sameError ratio 1.00; 1.00 is a tie.
Tests won: 5 / 6Complete recent tests excluded from model selection; ties count as no win.
Test period: 2020-09-01 to 2026-06-014 observations ahead per test.
80% band coverage: 84%Observed outcomes inside the band across all stored test windows.

Band coverage uses all stored test windows, including model-selection windows when present; it does not determine the confidence label.
Evaluated 2026-11-14 against the cached history through 2026-06-01.

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

5 of 5 models evaluatedModels are selected on older tests and evaluated on separate recent tests. All four classes completed their backtests.

Models tested

Exponential smoothing scored lowest on the 2 older tests (1.00) and was chosen. The 6 recent tests played no part in the choice and give its grade (1.00). The search for a model that beats the naive forecast on both sets of tests goes on. Scores are MASE: a model's errors divided by the naive forecast's, so 1 is a tie.

4 model classes completed the tests: Statistical, Machine learning, Foundation model, Neural network.

Every model tested on this series.
Model	Class and compute	Older tests	Recent tests	Role
Exponential smoothing (statistical)	Statistical · CPU	1.00	1.00	Serving
Naive forecast (last value held)	Benchmark · CPU	1.00	1.00	Benchmark
Gradient-boosted trees (machine learning)	Machine learning · CPU	1.01	0.94	Tested
State-space model, 167M (foundation model)	Foundation model · CPU	1.05	0.53	Tested
Agentic Investor TSFM (foundation model)	Foundation model · CPU	1.06	0.57	Tested
Hierarchical neural network (neural network)	Neural network · CPU	1.09	1.58	Tested

Uses ci-proto, released by Continuous Intelligence. Continuous Intelligence does not endorse or approve this product or service. (ci-proto is shown as State-space model, 167M.)

Reading

It is above 54% 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.
Period	Value
Q2 2026	0.9%
Q1 2026	0.6%
Q4 2025	0.4%
Q3 2025	0.6%
Q2 2025	2.0%
Q1 2025	1.6%
Q4 2024	0.9%
Q3 2024	1.1%
Q2 2024	-0.9%
Q1 2024	-0.9%
Q4 2023	0.1%
Q3 2023	0.2%
Q2 2023	1.2%
Q1 2023	2.1%
Q4 2022	0.6%
Q3 2022	1.5%
Q2 2022	1.8%
Q1 2022	1.1%
Q4 2021	2.7%
Q3 2021	3.4%
Q2 2021	8.6%
Q1 2021	0.2%
Q4 2020	-0.6%
Q3 2020	-5.1%

Source

Data: public market data feeds.