Japan Overtime Pay YoY

Name: Japan Overtime Pay YoY
Creator: Agentic Investor

Japan · Monthly series · EC-JP-OPYY

Latest, September 2026

3.6%

-1.30pp from August 2026

Open interactive chartJapan Overtime Pay YoY in the app

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

Forecast

Forecast for September 2027: -0.1%, with an 80% interval of -4.1% to 1.9%.

Forecast path. Intervals are shown where available.
Period	Forecast	80% low	80% high
October 2026	3.6%	2.1%	5.0%
November 2026	2.9%	0.8%	5.0%
December 2026	2.8%	0.9%	4.5%
January 2027	2.5%	-1.6%	6.1%
February 2027	1.9%	-1.8%	5.2%
March 2027	1.5%	-0.8%	3.3%
April 2027	1.1%	-1.8%	3.2%
May 2027	0.8%	-4.8%	4.3%
June 2027	0.7%	-3.3%	3.2%
July 2027	0.4%	-5.2%	3.4%
August 2027	-0.0%	-4.1%	2.1%
September 2027	-0.1%	-4.1%	1.9%

Model: Agentic Investor TSFM (foundation model). Forecast generated 2026-11-14.

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: +28%Beats the no-change baseline over 12 tests (12 tests, 7 won)
Direction: 59%No clear edge over a coin flip (83 of 141 calls right in 12 tests)
Turning points: 32 of 73Anticipated some turns (73 turns in 12 tests, 0 without a turn)

Error vs baseline: 28% lowerError ratio 0.72; 1.00 is a tie.
Tests won: 7 / 12Complete rolling backtests; ties count as no win.
Test period: 2014-10-01 to 2026-09-0112 observations ahead per test.
80% band coverage: 80%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 2026-09-01.
This is an evaluation of the serving model; it does not establish a winner across four model classes.

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 119 earlier readings since October 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
September 2026	3.6%
August 2026	4.9%
July 2026	4.5%
June 2026	3.4%
May 2026	2.8%
April 2026	4.8%
March 2026	3.1%
February 2026	3.0%
January 2026	3.2%
December 2025	1.5%
November 2025	1.2%
October 2025	2.1%
September 2025	1.0%
August 2025	0.4%
July 2025	3.0%
June 2025	0.5%
May 2025	1.4%
April 2025	1.3%
March 2025	-0.4%
February 2025	2.4%
January 2025	1.5%
December 2024	0.8%
November 2024	1.4%
October 2024	0.7%

Source

Data: public market data feeds.