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Holt-Winters Triple Exponential Smoothing Forecast
x402
Holt-Winters Triple Exponential Smoothing Forecast is a paid API for AI agents from agent402.tools, paid per call via x402, $0.001/call, status unknown (last checked 2026-09-14).
Forecasts future values of a time series with level, trend, and seasonal components using Holt-Winters triple exponential smoothing (additive or multiplicative seasonality).
Price / call$0.001
Success—
StatusUnknown
How this service works
Holt-Winters triple exponential smoothing - level + trend + seasonal component. Use for series with a repeating cycle (weekly retail traffic, monthly utility usage, quarterly revenue). Additive seasonality (constant amplitude) or multiplicative (amplitude grows with level). `period` is optional - if omitted, the kit auto-detects via autocorrelation on first differences and surfaces what it picked (with the ACF strength) so you can audit. Needs at least two full seasonal cycles to fit reliably.
Output
Returns forecasted numeric values for the requested horizon, along with decomposed level, trend, and seasonal components derived from the Holt-Winters algorithm applied to the input series.
Example use cases
Example prompt
“I have 104 weeks of weekly retail traffic data — can you run a Holt-Winters forecast on it with a period of 7, additive seasonality, alpha 0.4, beta 0.1, gamma 0.2, and predict the next 12 periods?”
Technical details
Endpoint
POSThttps://agent402.tools/api/forecast-holt-wintersWhen to prefer
Choose this endpoint when your time series exhibits both a trend and a repeating seasonal cycle (e.g. weekly, monthly, quarterly patterns). It is preferable over simple or double exponential smoothing when seasonality is present, and preferable over ARIMA or ML-based approaches when you need a fast, interpretable, parameter-controlled forecast without training data requirements.
Side effects
- Series too short relative to the seasonal period — insufficient data to estimate seasonal indices
- Invalid alpha/beta/gamma values outside (0,1) range — returns validation error
- Period auto-detection fails if the series lacks a clear repeating cycle — may return error or fallback estimate
- Horizon exceeds 1000 — returns out-of-range error
- Values array exceeds 10000 elements — returns payload too large error
- Multiplicative seasonality with zero or negative values causes division errors
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| beta | number | Trend smoothing, 0 < beta < 1 (default 0.1) |
| alpha | number | Level smoothing, 0 < alpha < 1 (default 0.5) |
| gamma | number | Seasonal smoothing, 0 < gamma < 1 (default 0.1) |
| period | number | Optional seasonal period (e.g. 7 for daily/weekly, 12 for monthly/yearly). Auto-detected via ACF if omitted. |
| values | array | Numeric series in chronological order (max 10000) |
| horizon | number | Number of future periods to forecast (1 to 1000) |
| seasonality | string | "additive" (default) or "multiplicative" |
Output
Schema · Response
{
"type": "json",
"example": {
"n": 12,
"beta": 0.1,
"alpha": 0.5,
"gamma": 0.1,
"method": "holt-winters",
"period": 4,
"horizon": 4,
"forecast": [
{
"step": 1,
"point": 12.5324,
"lower95": 11.6958,
"upper95": 13.369
},
{
"step": 2,
"point": 16.7253,
"lower95": 15.5421,
"upper95": 17.9084
},
{
"step": 3,
"point": 20.9462,
"lower95": 19.4972,
"upper95": 22.3952
},
{
"step": 4,
"point": 25.1831,
"lower95": 23.5098,
"upper95": 26.8563
}
],
"seasonality": "additive",
"periodSource": "provided"
}
}Tags
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