We are excited to announce that in Amazon Forecast, you can now start your forecast horizon at custom starting points, including on Sundays for weekly forecasts. This allows you to more closely align demand planning forecasts to local business practices and operational requirements.
Forecast is a fully managed service that uses statistical and machine learning (ML) algorithms to deliver highly accurate time series forecasts. It uses state-of-the-art algorithms to predict future time series data based on historical data, and requires no ML experience. Typical Forecast applications include resource planning for inventory, workforce staffing, and web traffic. In this post, we review a new option that allows you to align forecasts with business and demand cycles, while reducing operational cost by offloading aggregation workflows.
To optimize demand planning, forecasts need to align with business operations. Previously, starting points for forecasts were fixed: daily forecasts assumed demand starting at midnight each day, weekly predictions assumed Monday as the first day of the week, and monthly predictions started on the first day of each month. These predefined starting points presented two challenges. First, if your business cycle began at a different point than the fixed value, you had to manually aggregate forecasts to your required starting point. For example, if your business week began on a Sunday and you wanted to produce weekly forecasts, you had to manually aggregate daily forecasts to a Sunday–Saturday week. This additional work added cost and compute time, and presented opportunities for errors. Second, the training data and forecast periods weren’t consistent; if your data reflects a demand cycle that begins on Sundays, the predictor and forecast should also use Sunday as the starting point.
Custom forecast horizon starting points now align business operations and forecasts, eliminating the need for manual aggregation work and saving cost and compute. If you have a business week starting on Sundays, you can automatically aggregate daily data to generate weekly forecasts that begin on Sundays. Or you can begin daily forecasts starting at 9:00 AM. Predictors can now be aligned with your ground truth data, providing consistency between inputs and forecasts. Forecast horizon starting points are easily defined when training new predictors via the Forecast console or using Forecast APIs.
Define custom forecast horizon starting periods
The forecast horizon, also called frequency, is the length of time for which a forecast is made, and is bounded by a starting and ending point. In Forecast, you can now select specific starting points for daily, weekly, monthly, and yearly forecast horizons when training new predictors. These starting points—also called boundary values—are selected at one frequency unit finer than the forecast horizon, as shown in the following table.
Forecast frequency unit
Day of week
Monday through Sunday
Day of month
1 through 28
January through December
With custom starting points, you can align forecasts to start at specific points in time that match your business processes and ground truth data, for example, the month of May, the 15th of the month, Sundays, or 15:00 hours. For forecast horizons coarser than the provided time series frequency, Forecast aggregates the time series data based on the custom starting point. For example:
When generating daily forecasts from hourly data with a 9:00 AM starting period, forecasts are aggregated with hourly data each day between 9:00 AM to the following day at 8:00 AM
When generating weekly forecasts from daily data with a Sunday starting period, forecasts are aggregated with daily data each week from Sunday to the following Saturday
When generating monthly forecasts from daily data with a starting day of the 15th of the month, forecasts are aggregated with daily data from the 15th of the current month to the 14th of the next month
When generating yearly forecasts from monthly data with a starting month of May, forecasts are aggregated with monthly data from May of the current year to April of next year
Available forecast frequencies
The following screenshots show examples of custom daily, weekly, monthly, and yearly forecast frequencies and starting points (the Time alignment boundary field on the Forecast console).
Specify custom forecast horizon starting points
You can define custom forecast horizon starting points when creating a new predictor. The following steps demonstrate how to do this using the Forecast console. We also offer a sample notebook that provides an example of how to integrate this new setting into your workflows.
On the Forecast console, choose View dataset groups, and then Create dataset group.
Create your dataset group, a target time series dataset, and load your data.
You’re redirected to the Forecast console as your data is loaded.
After your target time series dataset is loaded into your dataset group and active, choose Start under Train a predictor.
In the Train predictor section, provide values for the Name, Forecast frequency, and Forecast horizon fields.
In the optional Time alignment boundary field, specify the starting point the predictor uses for the forecast.
The values in this list depend on the Forecast frequency value you choose. In this example, we create weekly forecasts with a 1-week horizon, with Sunday as the starting day of the week and of the forecast.
Provide other optional configurations as needed and choose Create.
After you create the predictor, you can create your forecast.
In the navigation pane, under your dataset group choose Predictors.
Select your new predictor.
Choose Create forecast.
Provide the necessary details and choose Start to create your forecast.
When the forecast is complete, choose Create forecast export to export the results.
The following screenshots are samples of the original input file (left) and the exported forecast results (right). The input file is at an hourly frequency, whereas the forecast is produced at a weekly frequency, beginning with Sunday as the first day of the week. This is an example of Forecast automatically aggregating over two levels of forecast frequencies (from hours to days).
Custom forecast horizon starting points in Forecast allow you to produce forecasts that align with your specific operational requirements. Work weeks start on different days in different regions, requiring forecasts that begin on days other than Mondays, and that are aligned with ground truth training and ongoing data. Or you may want to generate hourly forecasts that reflect a demand cycle beginning at 7:00 AM each day, for example.
Forecast also automatically aggregates fine-grained forecasts to higher-level frequencies (such as days into weeks). This allows you to produce forecasts aligned with your operations, and saves you costs by removing the need to stand up and manage aggregation workflows.
Custom starting points are optional. If you don’t provide specific starting points, forecasts start at default times. Specific forecast horizon starting points are only available with AutoPredictor. For more information, refer to New Amazon Forecast API that creates up to 40% more accurate forecasts and provides explainability and CreateAutoPredictor.
To learn more about forecast frequencies, refer to Data aggregation for different forecast frequencies. All these new capabilities are available in all Regions where Forecast is publicly available. For more information about Region availability, see AWS Regional Services.
About the Authors
Dan Sinnreich is a Sr. Product Manager for Amazon Forecast. He is focused on democratizing low-code/no-code machine learning and applying it to improve business outcomes. Outside of work, he can be found playing hockey, trying to improve his tennis serve, scuba diving, and reading science fiction.
Paras Arora is a Software Development Engineer in the Amazon Forecast Team. He is passionate about building cutting edge AI/ML solutions in the cloud. In his spare time, he enjoys hiking and traveling.
Chetan Surana is a Software Development Engineer in the Amazon Forecast team. His interests lie at the intersection of machine learning and software development, applying thoughtful design and engineering skills to solve problems. Outside of work, he enjoys photography, hiking, and cooking.
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