Build a forecast

Time series forecasting from gold or the AirPassengers sample.

Try the sample first

Quick start: sample walks through AirPassengers end-to-end.

Before you start

Path	You need
Your data	Gold with Forecast enabled, valid time + value columns
Sample	Nothing

Train from gold

1. Forecast → new Build
2. Select gold artifact
3. Forecast interval (hourly → quarterly)
4. Review analysis (seasonality, trend, algorithm hint)
5. Name → train

Train from sample

1. Sample dataset → AirPassengers
2. Interval → train

After training

• Deploy · Monitor

Troubleshooting

Analysis: invalid data
Republish gold with a clear datetime column and numeric values.

No gold in the list?

Confirm Forecast was enabled at publish and the artifact is ready (not draft or failed).

Wrong interval?

Monthly data trained as hourly produces poor forecasts. Match interval to how your series was collected.