World population · World · 2 years ahead

2027: the model assigns an 80 percent probability to 8,345,970,059 to 8,375,440,699 people

Point estimate 8,367,893,531 people from Holt (exponential smoothing with additive trend), against 8,215,424,893 people observed in 2025. holt beats last_value on out-of-sample MAE by 94.5 percent over 15 cutoffs (minimum 8, margin 5 percent).

ModeledMetric page

Distribution over the history

Fan chart

Solid: observed (people). 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 8,344,611,033 · p10 8,345,970,059 · p25 8,356,453,348 · p50 8,361,246,600 · p75 8,364,313,307 · p90 8,375,440,699 · p95 8,378,871,115
Source: empirical quantiles of 12 in-window 2-step residuals, in log space; no normality assumed

The record

Immutable fields

Key: world-population:WLD:2
Id: 74641f7e-af85-4baf-97ca-a518fbc88510
Created: 2026-09-07 22:16 UTC
Data cutoff: 2026-09-07 22:16 UTC
Origin: 2025 = 8,215,424,893 (obs 137382)
Target period: 2027 (730 days)
Point: 8,367,893,531 people
Interval: 8,345,970,059 to 8,375,440,699 at 80 percent
Model: holt models_v1.targets_v1
Baseline: last_value
Snapshot: 4f543ddaf61c09f5ea6a7cf6c536747ff16bb2b50bd82782b7e6f6704d47df21
Data mode: ingested_at
Status: active

Drivers

Statistical association, not causal

Holt (exponential smoothing with additive trend) uses only the target's own history; there are no driver contributions.

Outcome

Not yet realised

The observation for 2027 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 world-population 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 smoothed level and trend at the cutoff persist over the horizon.

Falsifiers

What would show this forecast wrong

If the first published value of world-population (WLD) for 2027 is below 8,345,970,059 people or above 8,375,440,699 people, the 80 percent interval is falsified.
The model assigns a 10 percent probability to a value below 8,345,970,059 people and a 10 percent probability to a value above 8,375,440,699 people; 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 (8,215,424,893 people); 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
Holt (exponential smoothing with additive trend) models_v1.targets_v1	champion	15	8,984,994	10,834,692	100%	27%	94.5%
Drift models_v1.targets_v1	challenger	15	85,607,977	87,696,122	100%	40%	47.4%
Last value models_v1.targets_v1	baseline	15	162,619,610	163,220,596	0%	100%	0.0%
Linear trend models_v1.targets_v1	challenger	15	28,681,362	34,741,889	100%	67%	82.4%
Ridge regression on lags and drivers models_v1.targets_v1	challenger	15	14,464,122	19,964,410	100%	40%	91.1%

Model card

Holt (exponential smoothing with additive trend)

Purpose. Point and 80 percent interval forecasts of world-population (WLD) 1, 2, 5 years ahead. Owner: Forecast Lab (HV 3.0).

Method

Simple exponential smoothing with an additive trend. Level and trend smoothing parameters (alpha 0.95, beta 0.95) are chosen by grid search on one-step-ahead squared error inside the training window. 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: 66 periods, 1960-01-01 to 2025-01-01. Evaluation: rolling-origin backtest over 15 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.