INVENTORY_PLANNING Domain
Use the INVENTORY_PLANNING domain to forecast raw material demand and determine how much inventory of a specific item to stock. It supports the dataset types listed below. We list essential and optional fields for each dataset type.
Target Time Series Dataset Type
The following fields must be filled out:
- item_id (string)
- timestamp (timestamp)
- demand (float) - Amazon Forecast provides a forecast for this target field.
The following dimension is optional and can be used to adjust the granularity of forecasting:
- location (string) - The distribution facility in which the item is stocked. Only use this if you have several stores/locations.
Related Time Series Dataset Type
The following fields must be filled out:
- item_id (string)
- timestamp (timestamp)
The following items are optional but may help improve forecast results:
- price (float) - The price of the item
Item Metadata Dataset Type
The following fields must be filled out:
The following items are optional but may help improve forecast results:
- category (string) - The item's category.
- brand (string) - The item's brand.
- lead_time (string) - The number of days required to manufacture the item.
- order_cycle (string) - The order cycle begins with the commencement of work and ends when the item is ready for delivery.
- safety_stock (string) - The bare minimum of inventory to have on hand for that item.
EC2 CAPACITY Domain
Amazon Forecast EC2 capacity using the EC2 CAPACITY domain. It supports the dataset types listed below. We list essential and optional fields for each dataset type.
Target Time Series Dataset Type
The following fields must be filled out:
- instance_type (string) – The instance's type (for example, c5.xlarge).
- timestamp (timestamp)
- number_of_instances (integer) – The number of instances of that specific instance type consumed at that moment. Amazon Forecast provides a forecast for this target field.
The following dimension is optional and can be used to adjust the granularity of forecasting:
- location (string) – An AWS Region, such as us-west-2 or us-east-1, can be provided. Only use this if you're modelling numerous Regions.
Related Time Series Dataset Type
The following fields must be filled out:
- instance_type (string)
- timestamp (timestamp)
WORK_FORCE Domain
Use the WORK_FORCE domain to forecast workforce demand. It supports the dataset types listed below. We list essential and optional fields for each dataset type.
Target Time Series Dataset Type
The following fields must be filled out:
- workforce_type (string) - The type of labour to be forecasted. For instance, contact center or fulfillment center labour need.
- timestamp (timestamp)
- workforce_demand (floating-point integer) - The target field for which Amazon Forecast produces a forecast.
The following dimension is optional and can be used to adjust the granularity of forecasting:
- location (string) - The location in which labour resources are searched. If you have many stores/locations, this should be used.
Related Time Series Dataset Type
The following fields must be filled out:
- workforce_type (string)
- timestamp (timestamp)
Item Metadata Dataset Type
The following fields must be filled out:
The following items are optional but may help improve forecast results:
- wages (float) – The average pay for that worker type.
- shift_length (string) – The shift's length.
- location (string) - The workforce's location.
WEB_TRAFFIC Domain
To forecast web traffic to a website or set of web properties, use the WEB TRAFFIC domain. It supports the dataset types listed below.
Target Time Series Dataset Type
The following fields must be filled out:
- item_id (string) — A unique identifier for each forecasted web property.
- timestamp (timestamp)
- value (float) – This is the target field for which Amazon Forecast makes a forecast.
Related Time Series Dataset Type
The following fields must be filled out:
- item_id (string)
- timestamp (timestamp)
Item Metadata Dataset Type
The following fields must be filled out:
The following field is optional but may help improve forecast results:
METRICS Domain
Forecast indicators like revenue, sales, and cash flow using the METRICS domain. It supports the dataset types listed below.
Target Time Series Dataset Type
The following fields must be filled out:
- metric_name (string)
- timestamp (timestamp)
- metric_value (floating-point integer) - The target field for which Amazon Forecast makes a forecast (for example, the amount of revenue generated on a particular day).
Related Time Series Dataset Type
The following fields are required:
- metric_name (string)
- timestamp (timestamp)
Item Metadata Dataset Type
The following fields must be filled out:
The following items are optional but may help improve forecast results:
Frequently Asked Questions
What method of forecasting Does Amazon use?
Amazon Forecast is a time-series forecasting service based on machine learning (ML) and built for business metrics analysis.
How does Amazon forecast its sales?
Amazon Forecast forecasts future business outcomes for FBA sellers, such as resource requirements, product demand, and financial success, using machine learning technologies. This fully-managed service's built-in technologies analyze your Amazon FBA historical data to find patterns and forecast future sales.
What is the P90 forecast on Amazon?
There's a 90% probability that weekly consumer demand will be at the level indicated and a 10% chance Amazon will buy more.
Which cases apply to forecasting?
Time-series forecasting has various applications in planning. The most popular application is to compare Predictive Planning's statistical projections to your own forecast.
What are three forecasting techniques?
Qualitative approaches, time series analysis and projection, and causal models are the three main types.
Conclusion
In this article, we have discussed the following topics:
- Features of Amazon Forecast
- Working of Amazon Forecast
- Predefined Dataset Domains and Dataset Types
- Using item metadata datasets
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