Beta

How Forecast All works. Illustrative data, not a live forecast. Read the method and current backtests below.

Methodology · updated Oct 2, 2026

How the forecasts are made, and how well they do

Every number on Forecastall comes from the same automatic process: public data in, an open forecasting model, a median and an 80% range out. Nobody adjusts a forecast by hand. Below is how that works and a backtest of how the model would have done over the last five years.

Read the dated backtest snapshot: where the nominal 80% range missed its target →

Data

14 October 2026 refresh: US monthly CPI now uses official BLS history through September (336.940 index; 3.7% 12-month change), released today. Both replay gates passed; recent errors at 3/6/12 months are 0.668/1.013/1.320 percentage points versus 0.726/1.075/1.675 for no change. Observed p10-p90 coverage is 91.2%/88.9%/91.8%, not a calibrated guarantee. Earlier 2016-2020 results remain unchanged, including the small 3-month benchmark loss. October 2025 remains missing. These monthly results are separate from the fixed annual cohort audit.

8 October 2026 refresh: Fed funds now uses direct Federal Reserve H.15 monthly history through September (3.75%); Germany industrial production uses revised Eurostat B-D history through August (90.7, 2021=100). In the latest five-year replay, both have higher error than persistence at 6 and 12 months. Fed funds 12-month p10-p90 coverage is 44.9%, not a calibrated 80% interval. The aggregate tables below retain their dated cohort snapshot.

11 October 2026 refresh: industrial production for France, Italy, Spain, the Netherlands and Poland now uses full revised Eurostat B-D history through August. These are the same seasonally and calendar-adjusted 2021=100 series, excluding construction. The five-year replay below each forecast page has been recalculated. France beats no-change at all three horizons; the other four have at least one horizon with higher model error. Ranges are model quantiles; observed coverage varies by country and horizon. The aggregate tables below still describe their dated cohort snapshot.

  • Monthly US CPI: one BLS 12-month-change model on the existing US inflation page, separate from annual CPI rankings and the cohort counts below.
  • FRED (Federal Reserve Bank of St. Louis): Exchange rates against the US dollar, US Treasury nominal and inflation-protected yields, WTI and Brent crude, copper, and stock indices. 25 published series.
  • World Bank: World Development Indicators: annual inflation, GDP growth, unemployment, GDP per capita and population. Commodity Price Data (Pink Sheet): monthly Gold price in USD per troy ounce, not a live spot quote. 200 published series.
  • U.S. Bureau of Labor Statistics (BLS): Direct monthly CPI history for the US inflation page; total nonfarm job openings (JOLTS), seasonally adjusted, thousands; total nonfarm payroll employment (CES), seasonally adjusted, thousands; and the CPS unemployment rate, seasonally adjusted. October 2025 unemployment was not collected; the gap is preserved. 3 published series.
  • Federal Reserve Board: H.15 effective federal funds rate, monthly average, from the complete direct SDMX release. This is not the FOMC target range. 1 published series.
  • U.S. Census Bureau: Advance US goods trade balance, Census basis, seasonally adjusted, displayed as a deficit in USD billions; and total construction spending, seasonally adjusted annual rate, USD billions. Previous observations are revised, not original vintages. 2 published series.
  • Eurostat: Monthly HICP price indices, unemployment, industrial production and retail trade volume. 26 published series.
  • U.S. Energy Information Administration (EIA): Henry Hub natural gas prices and U.S. retail electricity prices. 2 published series.
  • Central Bank of Chile (BCCh): Observed USD/CLP exchange rate. 1 published series.
  • Central Bank of Argentina (BCRA): Official USD/ARS exchange rate. 1 published series.

Each run stores the raw series in a versioned repository, so every forecast can be traced to the exact data it saw. Every page shows its source series, the last observation used and the date the forecast was produced.

Currencies 19Inflation 39GDP growth 40Interest rates 4Commodities 4Stock indices 2Energy 2Unemployment 40GDP per capita 40Population 40HICP price index 6Monthly unemployment 9Industrial production 6Retail trade volume 6Job openings 1Goods trade deficit 1Payroll employment 1Construction spending 1

Model

Forecasts come from TimesFM 2.5, a time-series foundation model from Google Research, released under the Apache-2.0 license (checkpoint google/timesfm-2.5-200m-pytorch, revision 1d952420fba8). It is used zero-shot: the model reads each series' own history and produces quantiles of the future path, without being trained or tuned on our data. We publish three of them: the 10th, 50th and 90th percentiles.

  • Median (p50): the middle of the model's distribution. Half of outcomes are expected above, half below.
  • 80% range (p10 to p90): the nominal interval between model quantiles p10 and p90. An 80% coverage target is not a guarantee; historical coverage varies by series and horizon.

The model sees one series at a time. It does not know about news, policy decisions or other series, which is both a limit and the reason it is consistent.

Horizons

  • Daily and monthly series (currencies, rates, commodities): 3, 6 and 12 months ahead. Daily series count trading days (63, 126, 252), so their target dates are approximate.
  • Annual series (World Bank): the next 1, 2 and 3 calendar years.
  • When a year has ended but the World Bank has not published it yet, its forecast is labeled a nowcast.
  • Charts of annual series draw a straight monthly line between the yearly forecast points. That line is display-only interpolation and is labeled as such; it is not a monthly forecast.

What we publish

A series is published only if its data is current and long enough to forecast. Today that leaves 261 forecast pages, including a separately validated monthly US CPI model on the existing US inflation page (262 forecast series). Left out:

  • Real interest rates (World Bank): missing or years out of date for most of the 40 countries.
  • Argentina inflation (World Bank CPI, annual %): too few recent observations in the World Bank series.

Forecasts are recomputed when new data arrives. Text on every page is a fixed template filled with that page's numbers; no page is written individually.

Current official observations

US monthly CPI: one additional BLS CPI-U forecast series (CUUR0000SA0), used as the primary forecast on the US inflation page. Annual WB CPI is retained only in the clearly labeled annual tables/rankings. Forecast input is the monthly 12-month percentage change. October 2025 remains missing, with an explicit decoder mask and no official imputation. See the US page for monthly backtests; these are not blended into the annual CPI family metrics. Source: BLS September 2026 release; public-domain reuse policy. Independent derived analysis is not a BLS forecast.

Backtest

To check the model we replay the past: at many dates over the last 5 years (21 trading days apart for daily series, 1 month for monthly, 1 year for annual) we give it only the data available at that date and compare its forecast with what actually happened. Two questions matter:

  1. Is the 80% range honest? If it is well calibrated, about 80% of outcomes fall inside it.
  2. Is the median better than doing nothing? We compare its error with a naive forecast that assumes no change from the last value, a benchmark that is famously hard to beat for exchange rates and prices.
Share of outcomes inside the 80% rangeby topic and horizon; the dashed line is the 80% target
40%50%60%70%80%90%100%CurrenciesCurrencies, 3 mo: 83% of 1082 outcomes inside the rangeCurrencies, 6 mo: 80% of 1025 outcomes inside the rangeCurrencies, 12 mo: 79% of 911 outcomes inside the rangeInflationInflation, 1 yr: 74% of 195 outcomes inside the rangeInflation, 2 yr: 59% of 156 outcomes inside the rangeInflation, 3 yr: 70% of 117 outcomes inside the rangeGDP growthGDP growth, 1 yr: 77% of 200 outcomes inside the rangeGDP growth, 2 yr: 85% of 160 outcomes inside the rangeGDP growth, 3 yr: 94% of 120 outcomes inside the rangeInterest ratesInterest rates, 3 mo: 81% of 172 outcomes inside the rangeInterest rates, 6 mo: 74% of 163 outcomes inside the rangeInterest rates, 12 mo: 68% of 145 outcomes inside the rangeCommoditiesCommodities, 3 mo: 84% of 173 outcomes inside the rangeCommodities, 6 mo: 79% of 164 outcomes inside the rangeCommodities, 12 mo: 86% of 146 outcomes inside the rangeStock indicesStock indices, 3 mo: 73% of 114 outcomes inside the rangeStock indices, 6 mo: 67% of 108 outcomes inside the rangeStock indices, 12 mo: 65% of 96 outcomes inside the rangeEnergyEnergy, 6 mo: 74% of 109 outcomes inside the rangeEnergy, 12 mo: 74% of 97 outcomes inside the rangeEnergy, 3 mo: 80% of 115 outcomes inside the rangeUnemploymentUnemployment, 1 yr: 83% of 200 outcomes inside the rangeUnemployment, 2 yr: 74% of 160 outcomes inside the rangeUnemployment, 3 yr: 66% of 120 outcomes inside the rangeGDP per capitaGDP per capita, 1 yr: 86% of 200 outcomes inside the rangeGDP per capita, 2 yr: 79% of 160 outcomes inside the rangeGDP per capita, 3 yr: 71% of 120 outcomes inside the rangePopulationPopulation, 1 yr: 85% of 200 outcomes inside the rangePopulation, 2 yr: 86% of 160 outcomes inside the rangePopulation, 3 yr: 80% of 120 outcomes inside the range40%60%80%100%CurrenciesCurrencies, 3 mo: 83% of 1082 outcomes inside the rangeCurrencies, 6 mo: 80% of 1025 outcomes inside the rangeCurrencies, 12 mo: 79% of 911 outcomes inside the rangeInflationInflation, 1 yr: 74% of 195 outcomes inside the rangeInflation, 2 yr: 59% of 156 outcomes inside the rangeInflation, 3 yr: 70% of 117 outcomes inside the rangeGDP growthGDP growth, 1 yr: 77% of 200 outcomes inside the rangeGDP growth, 2 yr: 85% of 160 outcomes inside the rangeGDP growth, 3 yr: 94% of 120 outcomes inside the rangeInterest ratesInterest rates, 3 mo: 81% of 172 outcomes inside the rangeInterest rates, 6 mo: 74% of 163 outcomes inside the rangeInterest rates, 12 mo: 68% of 145 outcomes inside the rangeCommoditiesCommodities, 3 mo: 84% of 173 outcomes inside the rangeCommodities, 6 mo: 79% of 164 outcomes inside the rangeCommodities, 12 mo: 86% of 146 outcomes inside the rangeStock indicesStock indices, 3 mo: 73% of 114 outcomes inside the rangeStock indices, 6 mo: 67% of 108 outcomes inside the rangeStock indices, 12 mo: 65% of 96 outcomes inside the rangeEnergyEnergy, 6 mo: 74% of 109 outcomes inside the rangeEnergy, 12 mo: 74% of 97 outcomes inside the rangeEnergy, 3 mo: 80% of 115 outcomes inside the rangeUnemploymentUnemployment, 1 yr: 83% of 200 outcomes inside the rangeUnemployment, 2 yr: 74% of 160 outcomes inside the rangeUnemployment, 3 yr: 66% of 120 outcomes inside the rangeGDP per capitaGDP per capita, 1 yr: 86% of 200 outcomes inside the rangeGDP per capita, 2 yr: 79% of 160 outcomes inside the rangeGDP per capita, 3 yr: 71% of 120 outcomes inside the rangePopulationPopulation, 1 yr: 85% of 200 outcomes inside the rangePopulation, 2 yr: 86% of 160 outcomes inside the rangePopulation, 3 yr: 80% of 120 outcomes inside the range

Nearest horizon (3 mo / 1 yr)Middle (6 mo / 2 yr)Farthest (12 mo / 3 yr)

Backtest results by topic

TopicHorizonSeriesTestsAvg. errorError vs naiveIn 80% range
Currencies3 mo191,0823.7%+1%83%
6 mo191,0255.4%+1%80%
12 mo199118.0%+2%79%
Inflation1 yr391952.80 pp+0%74%
2 yr391564.16 pp−11%59%
3 yr391173.25 pp−25%70%
GDP growth1 yr402002.47 pp−41%77%
2 yr401602.15 pp−56%85%
3 yr401201.72 pp−65%94%
Interest rates3 mo31720.39 pp−2%81%
6 mo31630.67 pp+2%74%
12 mo31451.11 pp+2%68%
Commodities3 mo317310.9%+3%84%
6 mo316414.5%+5%79%
12 mo314616.7%+1%86%
Stock indices3 mo2114mixed−7%73%
6 mo2108mixed−10%67%
12 mo296mixed−16%65%
Energy6 mo2109mixed−18%74%
12 mo297mixed−1%74%
3 mo2115mixed−22%80%
Unemployment1 yr402000.64 pp+1%83%
2 yr401601.11 pp+2%74%
3 yr401201.47 pp+10%66%
GDP per capita1 yr402007.7%−2%86%
2 yr4016011.3%−5%79%
3 yr4012015.8%−1%71%
Population1 yr402000.6%−36%85%
2 yr401600.8%−54%86%
3 yr401201.3%−50%80%

Avg. error: mean absolute % error for price-like series, mean absolute error in percentage points for rates. Error vs naive: geometric mean over series of the model's error divided by the no-change forecast's error, minus one; negative means the model was more accurate.

What the backtest says

  • Currencies: the median was about as accurate as a no-change forecast on average across horizons, and the 80% range contained 81% of outcomes.
  • Inflation: the median was more accurate than a no-change forecast on average across horizons, and the 80% range contained 68% of outcomes.
  • GDP growth: the median was more accurate than a no-change forecast on average across horizons, and the 80% range contained 85% of outcomes.
  • Interest rates: the median was about as accurate as a no-change forecast on average across horizons, and the 80% range contained 75% of outcomes.
  • Commodities: the median was about as accurate as a no-change forecast on average across horizons, and the 80% range contained 83% of outcomes.
  • Stock indices: the median was more accurate than a no-change forecast on average across horizons, and the 80% range contained 68% of outcomes.
  • Energy: the median was more accurate than a no-change forecast on average across horizons, and the 80% range contained 76% of outcomes.
  • Unemployment: the median was about as accurate as a no-change forecast on average across horizons, and the 80% range contained 74% of outcomes.
  • GDP per capita: the median was about as accurate as a no-change forecast on average across horizons, and the 80% range contained 79% of outcomes.
  • Population: the median was more accurate than a no-change forecast on average across horizons, and the 80% range contained 83% of outcomes.

For currencies and commodities, matching the no-change forecast is the realistic goal: markets price in what is known, so the value of these pages is mostly the range, which tells you how far the price could plausibly move.

Limits

  • The model only sees the history of each series. Shocks it cannot know about (a devaluation, a war, a policy change) will not be in the forecast.
  • Annual World Bank series are short and revised after publication; their backtests rest on few test points.
  • The nominal 80% range is a model target, not a calibrated guarantee. Backtests show that misses can be much more frequent for some series and horizons.
  • Forecasts are statistical output, not investment, tax or policy advice.

Questions or a series you'd like to see? Request a forecast. Model and data licenses: TimesFM (Apache-2.0); FRED and World Bank data under their respective terms of use.