Forecast

Train a time series from gold published for Forecast, with 30+ rows and a datetime column, or from the AirPassengers sample.

Data Intelligence → Forecast (/forecast)

Compared to ML and Loop

	Forecast	ML	Loop
Question	What happens next in time?	Given features, what value/class?	Adapt from live feedback
Shape	Datetime + series	Features + target	Loop schema

Path

1. CSV with date column → bronze
2. Publish gold for Forecast
3. Build a forecast
4. Deploy → Monitor

The sample path is on Build a forecast. A question about text, with answers you write, is Resolve.

Common mistakes

Mistake	Fix
Gold published as ML	Use Forecast use case at publish
Unparseable dates	Fix the time column before publish

Next

• Build a forecast
• Forecast API integration