US consumer price index, all items · United States · 12 months ahead

2027-07: the model assigns an 80 percent probability to 340.2 to 344.7 index 1982-1984=100

Point estimate 341 index 1982-1984=100 from Ridge regression on lags and drivers, against 332.8 index 1982-1984=100 observed in 2026-07. ridge beats last_value on out-of-sample MAE by 81.2 percent over 36 cutoffs (minimum 24, margin 5 percent).

ModeledMetric page

Distribution over the history

Fan chart

Solid: observed (index 1982-1984=100). Dashed and shaded: model point estimates and 80 percent intervals from empirical residual quantiles. Filled dot: the record on this page. Green or red dot: the realised value, inside or outside its interval.

Quantiles: p5 340.1 · p10 340.2 · p25 341 · p50 342.3 · p75 343.8 · p90 344.7 · p95 346.1
Source: empirical quantiles of 35 in-window 12-step residuals, in log space; no normality assumed

The record

Immutable fields

Key: us-cpi-all-items:USA:12
Id: 9a0aadeb-5d89-4ab0-85be-27cca4b31160
Created: 2026-09-07 22:16 UTC
Data cutoff: 2026-09-07 22:16 UTC
Origin: 2026-07 = 332.8 (obs 167372)
Target period: 2027-07 (365 days)
Point: 341 index 1982-1984=100
Interval: 340.2 to 344.7 at 80 percent
Model: ridge models_v1.targets_v1
Baseline: last_value
Snapshot: e5575d95d4958effa88c375414bf653b138b76bc8e7091abe74772c7bf5b4260
Data mode: ingested_at
Status: active

Drivers

Statistical association, not causal

Ridge coefficient times the standardised input at the origin, in log index 1982-1984=100. These are associations learned in the training window, labelled as such; nothing here is a causal effect.

Feature	Input	Coef	Contribution
us-cpi-energy[t-3]	326	-0.0019	-0.0050
us-cpi-energy[t-1]	319.3	-0.0018	-0.0046
us-ppi-all-commodities[t-3]	282.8	-0.0009	-0.0023
us-ppi-all-commodities[t-1]	286.3	-0.0007	-0.0019
us-nonfarm-payrolls[t-1]	158,892	0.0006	+0.0010
us-nonfarm-payrolls[t-3]	158,798	0.0005	+0.0009
y[t-0]	5.81	0.0006	+0.0009
y[t-1]	5.81	0.0005	+0.0008
y[t-2]	5.81	0.0005	+0.0008
y[t-11]	5.78	0.0001	+0.0002

Outcome

Not yet realised

The observation for 2027-07 has not been ingested. The weekly forecast.evaluate job attaches it when it arrives; the record above does not change.

Scenario assumptions

What the forecast takes for granted

No structural break in us-cpi between the data cutoff and the target period.
The source keeps publishing the series on the same definition; a rebasing or redefinition invalidates the comparison.
The lagged statistical associations estimated in the training window hold over the horizon.
Driver series used, lagged: us-cpi-energy, us-ppi-all-commodities, us-nonfarm-payrolls. Their own future values are not forecast; only values known at the cutoff enter.

Falsifiers

What would show this forecast wrong

If the first published value of us-cpi (USA) for 2027-07 is below 340.2 index 1982-1984=100 or above 344.7 index 1982-1984=100, the 80 percent interval is falsified.
The model assigns a 10 percent probability to a value below 340.2 index 1982-1984=100 and a 10 percent probability to a value above 344.7 index 1982-1984=100; interval misses should occur about one time in five over many forecasts, and a run of misses well above that rate falsifies the calibration.
The point forecast is above the last observed value (332.8 index 1982-1984=100); a realised value at or below that level falsifies the direction call.

Competing models

Scorecard for this target and horizon

Model	Status	Cutoffs	MAE	RMSE	Direction	80% coverage	Skill vs last value
Ridge regression on lags and drivers models_v1.targets_v1	champion	36	1.75	2.26	100%	81%	81.2%
Drift models_v1.targets_v1	challenger	36	1.83	2.13	100%	61%	80.3%
Holt (exponential smoothing with additive trend) models_v1.targets_v1	challenger	36	3.23	4.23	100%	75%	65.2%
Last value models_v1.targets_v1	baseline	36	9.29	9.39	0%	100%	0.0%
Linear trend models_v1.targets_v1	challenger	36	9.35	11.05	100%	44%	-0.6%
Seasonal naive models_v1.targets_v1	challenger	36	9.29	9.39	0%	100%	0.0%

Model card

Ridge regression on lags and drivers

Purpose. Point and 80 percent interval forecasts of us-cpi (USA) 1, 3, 6, 12 months ahead. Owner: Forecast Lab (HV 3.0).

Method

A direct multi-horizon ridge regression: the change from the origin to the target period is regressed on lagged target values (lags [1,2,3,12]) and lagged driver series (lags [1,3]), features standardised, penalty lambda 1. Drivers: us-cpi-energy, us-ppi-all-commodities, us-nonfarm-payrolls. Every driver enters lagged, so no feature uses a period after the origin. The series is modelled in natural logarithms and transformed back, so intervals are asymmetric in level terms.

Uncertainty

Intervals are empirical: the model is refitted at earlier origins inside the training window, the realised errors at the same horizon are collected, and the interval is the 10th to 90th percentile of those errors around the point forecast. No normality is assumed.

Training and evaluation

Training window: 954 periods, 1947-01-01 to 2026-07-01. Evaluation: rolling-origin backtest over 36 cutoffs using pseudo real-time data (the earliest ingested value per period; no vintage archive exists for this series, so revisions between the cutoff and today are not reproduced).

Known limitations

Structural breaks after the cutoff are not modelled.
Driver contributions are statistical associations, not causal effects.
Promotion to champion requires beating the last-value baseline on out-of-sample MAE by at least 5 percent over the minimum number of cutoffs; otherwise the baseline stays champion.
Retraining: every scheduled forecast.run refits on the data then available; backtests re-run from /admin/forecasts.

Version chain

Supersedes

This is the first record for its key.