predict_forecast
Read-onlyIdempotent
Forecast future values from historical time series data using ARIMA or Holt-Winters models. Provides point forecasts with confidence bands for demand planning, KPI projection, and capacity forecasting.
Instructions
[Premium] Project future values from a univariate time series using ARIMA or Holt-Winters (additive seasonal). Use for short-to-medium horizon point forecasts with confidence bands: demand planning, KPI projection, capacity forecasting. ARIMA suits non-seasonal trend data; Holt-Winters handles repeating seasonality (set seasonLength). Needs at least ~20 observations for stable fit. For point-anomaly flags rather than projection, use detect_anomaly. Requires ORACLAW_API_KEY.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | Historical values, evenly spaced. ARIMA needs ≥20 points; Holt-Winters needs ≥2 × seasonLength. | |
| steps | Yes | Number of future periods to forecast. | |
| method | No | Default: arima. | |
| seasonLength | No | Period of seasonality (only used by holt-winters). Default: 4. |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| forecast | Yes | Point forecasts, length = steps. | |
| confidence | No | ||
| model | No | Fitted model description. | |
| method | Yes | ||
| inputLength | No | ||
| steps | Yes |