AgenticInvestor

United States Factory Orders

United States · Monthly series · EC-US-FO

Latest, September 2026

1.8 pts

+500.0% from August 2026

Open interactive chartUnited States Factory Orders in the app

8.3-2.85-14October 2016September 2027
History through September 2026, the forecast path to September 2027 (dashed) and its 80% interval (shaded).

Forecast

Forecast for September 2027: 0.3 pts, with an 80% interval of -1.7 pts to 2.3 pts.

Forecast path. Intervals are shown where available.
PeriodForecast80% low80% high
October 20260.3 pts-3.6 pts4.2 pts
November 20260.3 pts-3.6 pts4.2 pts
December 20260.3 pts-3.3 pts3.9 pts
January 20270.3 pts-3.0 pts3.6 pts
February 20270.3 pts-0.7 pts1.3 pts
March 20270.3 pts-3.3 pts3.9 pts
April 20270.3 pts-2.0 pts2.6 pts
May 20270.3 pts-2.6 pts3.2 pts
June 20270.3 pts-4.5 pts5.1 pts
July 20270.3 pts-3.5 pts4.1 pts
August 20270.3 pts-2.0 pts2.6 pts
September 20270.3 pts-1.7 pts2.3 pts

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

AI confidence

Needs refresh

The data refresh is due. Confidence will be reassessed against the refreshed history.

Model: Exponential smoothing (statistical)

Accuracy vs naive
n/aNot evaluated
Direction
n/aNot evaluated
Turning points
n/aNot evaluated
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

Previous comparisonThe stored comparison uses earlier data or a previous model roster. An eligible nightly batch can compare both candidates, subject to call budgets and retry limits.

Models tested

Exponential smoothing scored lowest on the 6 older tests (0.94) and was chosen. The 6 recent tests played no part in the choice and give its grade (0.91). 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, Foundation model, Machine learning, Neural network. Previous model roster.

Every model tested on this series.
ModelClass and computeOlder testsRecent testsRole
Exponential smoothing (statistical)Statistical · CPU0.940.91Serving
State-space model, 167M (foundation model)Foundation model · CPU0.940.83Tested
Agentic Investor TSFM (foundation model)Foundation model · CPU0.950.80Tested
Naive forecast (last value held)Benchmark · CPU1.001.00Benchmark
Gradient-boosted trees (machine learning)Machine learning · CPU1.041.14Tested
Hierarchical neural network (neural network)Neural network · CPU1.051.09Tested

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 78% 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.
PeriodValue
September 20261.8 pts
August 20260.3 pts
July 20260.8 pts
June 2026-0.2 pts
May 2026-1.1 pts
April 20265.3 pts
March 20261.8 pts
February 20260.3 pts
January 20260.0 pts
December 2025-0.4 pts
November 20252.7 pts
October 2025-1.2 pts
September 20250.2 pts
August 20251.3 pts
July 2025-1.3 pts
June 2025-4.8 pts
May 20258.3 pts
April 2025-3.9 pts
March 20253.4 pts
February 20250.5 pts
January 20250.3 pts
December 20240.4 pts
November 2024-0.3 pts
October 20240.0 pts

Source

Data: public market data feeds.

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