How Aggregation Works

During training, Amazon Forecast aggregates any data that does not align with the forecast frequency you specify. For example, you might have some daily data but specify a weekly forecast frequency. Forecast aligns the daily data based on the week that it belongs in. Forecast then combines it into single record for each week. Forecast determines what week (or month or day and so on) data belongs in based on its relation to a time boundary. Time boundaries specify the beginning of a unit of time, such as what hour a day begins or what day a week begins.

For hourly and minutely forecasts, or unspecified time boundaries, Forecast uses a default time boundary based on your frequency's unit of time. For auto predictors with daily, weekly, monthly, or yearly forecast frequencies, you can specify a custom time boundary. For more information about time boundaries, see Time Boundaries.

During aggregation, the default transformation method is to sum the data. You can configure the transformation when you create your predictor. You do this in the Input data configuration section on the Create predictor page in the Forecast console. Or you can set the transformation method in the Transformations parameter in the AttributeConfig of the CreateAutoPredictor operation.

The following tables show an example aggregation for an hourly forecast frequency using the default time boundary: Each hour begins at the top of the hour.

Pre-transformation

Time	Data	At Top of the Hour
2018-03-03 01:00:00	100	Yes
2018-03-03 02:20:00	50	No
2018-03-03 02:45:00	20	No
2018-03-03 04:00:00	120	Yes

Post-transformation

Time	Data	Notes
2018-03-03 01:00:00	100
2018-03-03 02:00:00	70	Sum of the values between 02:00:00-02:59:59 (50 + 20)
2018-03-03 03:00:00	Empty	No values between 03:00:00-03:59:59
2018-03-03 04:00:00	120

The following figure shows how Forecast transforms data to fit the default weekly time boundary.