HUMANITYVECTOR

US consumer price index, all items · United States · 1 month ahead

2026-09: the model assigns an 80 percent probability to 334.3 to 335.5 index 1982-1984=100

Point estimate 334.8 index 1982-1984=100 from Ridge regression on lags and drivers, against 334.1 index 1982-1984=100 observed in 2026-08. ridge beats last_value on out-of-sample MAE by 48.3 percent over 46 cutoffs (minimum 24, margin 5 percent).

Distribution over the history

Fan chart

60 observed points to 2026-08, then 4 forecasts with 80 percent intervals272.7309.3345.92021-082022-042022-122023-082024-042024-122025-082026-052027-08
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 334.1 · p10 334.3 · p25 334.6 · p50 334.9 · p75 335.2 · p90 335.5 · p95 335.8
Source
empirical quantiles of 35 in-window 1-step residuals, in log space; no normality assumed
The record

Immutable fields

Key
us-cpi-all-items:USA:1
Id
772b00ed-68bd-4fe5-829b-43655c33c9f9
Created
2026-10-01 00:03 UTC
Data cutoff
2026-10-01 00:03 UTC
Origin
2026-08 = 334.1 (obs 409148)
Target period
2026-09 (31 days)
Point
334.8 index 1982-1984=100
Interval
334.3 to 335.5 at 80 percent
Model
ridge models_v1.targets_v1
Baseline
last_value
Snapshot
fb3a2b966753e506c7926d0a5d8f34a9986c33d90a8675f3a996268faebc776f
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.

FeatureInputCoefContribution
us-cpi-energy[t-3]338.6-0.0001-0.0004
us-cpi-energy[t-1]314.6-0.0001-0.0003
us-ppi-all-commodities[t-3]290.2-0.0001-0.0002
us-ppi-all-commodities[t-1]285.2-0.0000-0.0001
y[t-0]5.810.0001+0.0001
us-nonfarm-payrolls[t-1]158,9130.0000+0.0001
y[t-1]5.810.0000+0.0001
us-nonfarm-payrolls[t-3]158,8610.0000+0.0001
y[t-2]5.810.0000+0.0001
y[t-11]5.780.0000+0.0000
Outcome

Realised

Realised
336.3 index 1982-1984=100
Abs error
1.45
Squared
2.11
Direction
correct
Interval
missed
Evaluated
2026-10-19 00:22 UTC (eval_v1)

Postmortem

Interval miss: realised 336.3 index 1982-1984=100 is above the upper bound 335.5 index 1982-1984=100 by 0.7627 index 1982-1984=100. Model ridge; error 1.45 index 1982-1984=100 (0.43 percent of the origin value). The surprise came from the target itself moving outside the range of its recent lagged-driver errors; a structural break after the cutoff is possible and is not ruled out by the record. Assumption "no structural break between cutoff and target" is the first suspect.

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 2026-09 is below 334.3 index 1982-1984=100 or above 335.5 index 1982-1984=100, the 80 percent interval is falsified.
  • The model assigns a 10 percent probability to a value below 334.3 index 1982-1984=100 and a 10 percent probability to a value above 335.5 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 (334.1 index 1982-1984=100); a realised value at or below that level falsifies the direction call.
Competing models

Scorecard for this target and horizon

ModelStatusCutoffsMAERMSEDirection80% coverageSkill vs last value
Ridge regression on lags and drivers
models_v1.targets_v1
champion460.46380.656596%85%48.3%
Drift
models_v1.targets_v1
challenger460.4640.646796%83%48.3%
Holt (exponential smoothing with additive trend)
models_v1.targets_v1
challenger460.49190.721296%91%45.2%
Last value
models_v1.targets_v1
baseline460.89781.050%89%0.0%
Linear trend
models_v1.targets_v1
challenger463.213.8883%65%-258.0%
Seasonal naive
models_v1.targets_v1
challenger4610.9311.554%96%-1117.7%
Champion: Ridge regression on lags and drivers. ridge beats last_value on out-of-sample MAE by 48.3 percent over 46 cutoffs (minimum 24, margin 5 percent).
Model card

Ridge regression on lags and drivers

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: 955 periods, 1947-01-01 to 2026-08-01. Evaluation: rolling-origin backtest over 46 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

  1. 2a386143 2026-09-07 22:16 UTC · ridge · 333.5 [333, 334.3] · superseded

this version: new data at cutoff