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Forecasting Bake-Off — Multi-Method Time Series Forecast with Auto-Selection
x402
Forecasting Bake-Off — Multi-Method Time Series Forecast with Auto-Selection is a paid API for AI agents from agent402.tools, paid per call via x402, $0.014/call, status unknown (last checked 2026-09-14).
Backtests four forecasting methods (naive/drift, SES, Holt, Holt-Winters) on a given time series, ranks them by out-of-sample RMSE, and forecasts forward using the best-performing model.
Price / call$0.014
Success—
StatusUnknown
How this service works
Bundled execution of the Forecasting bake-off workflow - Don't guess which forecasting method to trust. Backtest all four (naive/drift, SES, Holt, Holt-Winters) on a real series, rank by out-of-sample RMSE, then forecast forward with the winner and its 95% prediction interval. Method selection without the hand-waving. One x402 payment runs 7 underlying tools (stock-history, fred-series, forecast-eval, forecast-naive, forecast-ses, forecast-holt, forecast-holt-winters); partial-success per step.
Output
Returns a ranked comparison of all four forecasting models by out-of-sample RMSE, identifies the winner, and provides the forward forecast values for the specified horizon using the best-performing method.
Example use cases
Example prompt
“Run the forecasting bake-off on AAPL — backtest all four methods (naive, SES, Holt, Holt-Winters) and then project 30 periods forward using whichever model had the lowest out-of-sample RMSE.”
Technical details
Endpoint
POSThttps://agent402.tools/api/skill/forecasting-bake-offWhen to prefer
Use this endpoint when you need to objectively select the best forecasting model rather than assuming one method fits all. Ideal for financial tickers (e.g. AAPL) and FRED macroeconomic series (e.g. UNRATE, CPIAUCSL) when you want out-of-sample validation before committing to a forecast, rather than hand-picking a model. Prefer this over single-model forecast endpoints when accuracy and model selection rigor matter.
Side effects
- Invalid or unrecognized ticker symbol — series not found
- FRED series ID does not exist or is unavailable
- Insufficient historical data to run a meaningful backtest
- Non-numeric or malformed horizon value
- Downstream data provider timeout or rate limit
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| series | string | What to forecast - a ticker (AAPL) or a FRED series id (UNRATE, CPIAUCSL) |
| horizon | string | How many periods to project forward - e.g. 30 (days for daily data, months for monthly) |
Output
Schema · Response
{
"type": "json",
"example": {
"args": {
"series": "AAPL",
"horizon": "30"
},
"pack": "forecasting-bake-off",
"steps": [
{
"ok": true,
"slug": "stock-history",
"result": {}
},
{
"ok": true,
"slug": "fred-series",
"result": {}
},
{
"ok": true,
"slug": "forecast-eval",
"result": {}
},
{
"ok": true,
"slug": "forecast-naive",
"result": {}
},
{
"ok": true,
"slug": "forecast-ses",
"result": {}
},
{
"ok": true,
"slug": "forecast-holt",
"result": {}
},
{
"ok": true,
"slug": "forecast-holt-winters",
"result": {}
}
],
"summary": "7/7 steps succeeded"
}
}Tags
x402
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