Forecast

Train time series models from gold datasets with a datetime column and a numeric value, or try the AirPassengers sample.

Before you start

Path	Need
Your data	Gold with Forecast use case, 30+ rows, parseable datetime
Sample	AirPassengers in the wizard

Sidebar → 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

Or quick start with samples.

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