China Inflation Rate YoY

China · Monthly series · EC-CN-IRYY

Latest, October 2026

0.7%

-0.20pp from September 2026

Open interactive chartChina Inflation Rate YoY in the app

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

Forecast

Forecast for October 2027: 0.9%, with an 80% interval of 0.1% to 1.9%.

Forecast path. Intervals are shown where available.
Period	Forecast	80% low	80% high
November 2026	0.7%	-0.1%	1.5%
December 2026	0.7%	0.2%	1.3%
January 2027	0.8%	-0.6%	2.1%
February 2027	0.8%	-1.4%	2.9%
March 2027	0.8%	-0.1%	1.8%
April 2027	0.8%	0.0%	1.6%
May 2027	0.8%	-0.2%	1.9%
June 2027	0.9%	-0.3%	2.1%
July 2027	0.9%	0.1%	1.8%
August 2027	0.9%	0.2%	1.7%
September 2027	0.9%	-0.1%	2.0%
October 2027	0.9%	0.1%	1.9%

Model: State-space model, 167M (foundation model). Forecast generated 2026-11-14.

AI confidence

Mixed evidence

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

Model: State-space model, 167M (foundation model)

Accuracy vs naive: +5%Matches the no-change baseline over 6 tests (6 tests, 4 won)
Direction: 54%No clear edge over a coin flip (37 of 68 calls right in 6 tests)
Turning points: 3 of 36Anticipated some turns (36 turns in 6 tests, 0 without a turn)

Error vs baseline: 5% lowerError ratio 0.95; 1.00 is a tie.
Tests won: 4 / 6Complete recent tests excluded from model selection; ties count as no win.
Test period: 2020-11-01 to 2026-10-0112 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 2026-10-01.

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

State-space model, 167M scored lowest on the 6 older tests (0.87) and was chosen. The 6 recent tests played no part in the choice and give its grade (0.95). 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: Foundation model, Statistical, Neural network, Machine learning.

Every model tested on this series.
Model	Class and compute	Older tests	Recent tests	Role
State-space model, 167M (foundation model)	Foundation model · CPU	0.87	0.95	Serving
Exponential smoothing (statistical)	Statistical · CPU	1.00	1.02	Tested
Naive forecast (last value held)	Benchmark · CPU	1.00	1.00	Benchmark
Agentic Investor TSFM (foundation model)	Foundation model · CPU	1.11	0.94	Tested
Hierarchical neural network (neural network)	Neural network · CPU	1.11	1.47	Tested
Gradient-boosted trees (machine learning)	Machine learning · CPU	1.31	0.92	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 32% of the 119 earlier readings since November 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
October 2026	0.7%
September 2026	0.9%
August 2026	0.8%
July 2026	0.5%
June 2026	1.0%
May 2026	1.2%
April 2026	1.2%
March 2026	1.0%
February 2026	1.3%
January 2026	0.2%
December 2025	0.8%
November 2025	0.7%
October 2025	0.2%
September 2025	-0.3%
August 2025	-0.4%
July 2025	0.0%
June 2025	0.1%
May 2025	-0.1%
April 2025	-0.1%
March 2025	-0.1%
February 2025	-0.7%
January 2025	0.5%
December 2024	0.1%
November 2024	0.2%

Source

Data: public market data feeds.