AgenticChina Interest Rate
China · Monthly series · EC-CN-INTR
Latest, October 2026
3.0%
+0.00pp from September 2026
Forecast
Forecast for October 2027: 3.0%, with an 80% interval of 2.4% to 3.2%.
| Period | Forecast | 80% low | 80% high |
|---|---|---|---|
| November 2026 | 3.0% | 3.0% | 3.0% |
| December 2026 | 3.0% | 2.9% | 3.0% |
| January 2027 | 3.0% | 2.9% | 3.0% |
| February 2027 | 3.0% | 2.8% | 3.0% |
| March 2027 | 3.0% | 2.7% | 3.0% |
| April 2027 | 3.0% | 2.6% | 3.0% |
| May 2027 | 3.0% | 2.7% | 3.0% |
| June 2027 | 3.0% | 2.5% | 3.1% |
| July 2027 | 3.0% | 2.6% | 3.1% |
| August 2027 | 3.0% | 2.3% | 3.2% |
| September 2027 | 3.0% | 2.5% | 3.2% |
| October 2027 | 3.0% | 2.4% | 3.2% |
AI confidence
Weak evidenceThe serving forecast made larger errors than the no-change baseline in these tests.
Model: Hierarchical neural network (neural network)
- Accuracy vs naive
- -98%Trails the no-change baseline over 6 tests (6 tests, 2 won)
- Direction
- 73%No clear edge over a coin flip (19 of 26 calls right in 4 tests)
- Turning points
- n/aToo few tests (0 turns, 8 needed) (0 turns in 6 tests, 0 without a turn)
- Error vs baseline
- 98% higherError ratio 1.98; 1.00 is a tie.
- Tests won
- 2 / 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
- 79%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.
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
Hierarchical neural network scored lowest on the 4 older tests (0.67) and was chosen. The 6 recent tests played no part in the choice and give its grade (1.98). 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: Neural network, Statistical, Foundation model, Machine learning.
| Model | Class and compute | Older tests | Recent tests | Role |
|---|---|---|---|---|
| Hierarchical neural network (neural network) | Neural network · CPU | 0.67 | 1.98 | Serving |
| Exponential smoothing (statistical) | Statistical · CPU | 0.75 | 1.75 | Tested |
| Agentic Investor TSFM (foundation model) | Foundation model · CPU | 0.94 | 1.20 | Tested |
| State-space model, 167M (foundation model) | Foundation model · CPU | 0.97 | 1.20 | Tested |
| Naive forecast (last value held) | Benchmark · CPU | 1.00 | 1.00 | Benchmark |
| Gradient-boosted trees (machine learning) | Machine learning · CPU | 2.84 | 1.72 | Tested |
Reading
It is above 0% of the 119 earlier readings since November 2016.
Recent history (24 observations)
| Period | Value |
|---|---|
| October 2026 | 3.0% |
| September 2026 | 3.0% |
| August 2026 | 3.0% |
| July 2026 | 3.0% |
| June 2026 | 3.0% |
| May 2026 | 3.0% |
| April 2026 | 3.0% |
| March 2026 | 3.0% |
| February 2026 | 3.0% |
| January 2026 | 3.0% |
| December 2025 | 3.0% |
| November 2025 | 3.0% |
| October 2025 | 3.0% |
| September 2025 | 3.0% |
| August 2025 | 3.0% |
| July 2025 | 3.0% |
| June 2025 | 3.0% |
| May 2025 | 3.0% |
| April 2025 | 3.1% |
| March 2025 | 3.1% |
| February 2025 | 3.1% |
| January 2025 | 3.1% |
| December 2024 | 3.1% |
| November 2024 | 3.1% |
Source
Data: public market data feeds.
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