The time scale of your historical and related datasets are taken from the calendar types you selected in your source data.
Time intervals for forecast horizons depend on the time scales given in the data collection. The time scales from the historical and related datasets determines supported time intervals for the forecast horizon. See the table below:
|Historical data time scale||Related data time scale||Possible time intervals for a forecast horizon|
Time scale aggregation
Historical data can be aggregated from lower to higher time scales, like days to weeks, or months.
Related data cannot be aggregated from lower to higher time scales, as it contains different types of data.
For example, for historical data, you can aggregate historical actuals like units sold. You cannot aggregate related data like daily prices into weekly prices, or store closures by day into store closures by week.
Time intervals for forecast models
The possible time intervals for a forecast model are dependent on the related data time scale.
If you only provide historical data, the time intervals of the forecast model must be equal to or greater than the time scale in the historical data.
If you provide both historical and related data, the time intervals for the forecast model must be:
- Equal to the related data time scale
- Equal or greater than the historical data time scale