Posted On: Dec 8, 2020

We’re excited to announce the Amazon Forecast Weather Index, which can increase your forecasting accuracy, by automatically including the latest local weather information to your demand forecasts with one click and at no extra cost. Weather conditions influence consumer demand patterns, product merchandizing decisions, staffing requirements and energy consumption needs; however, acquiring, cleaning, and effectively using live weather information for demand forecasting is challenging and requires ongoing maintenance. With this launch, you can now include 14-day weather forecasts for US and Europe locations with one click to your demand forecasts.  

The Amazon Forecast Weather Index combines multiple weather metrics from historical weather events and current forecasts at a given location to increase your demand forecast model accuracy. Amazon Forecast uses machine learning to generate more accurate demand forecasts, without requiring any prior ML experience. Forecast brings the same technology used at to developers as a fully managed service, removing the need for developers to manage resources or re-build their systems. 

Changes in local weather conditions can impact short term demand for products and services at particular locations for many customers in retail, hospitality, travel, entertainment, insurance and energy domains. While historical demand patterns show seasonal demand, advance planning for day-to-day variation is harder. In retail inventory management use cases, day-to-day weather variation impacts foot traffic and product mix. Typical demand forecasting systems do not take expected weather conditions into account, leading to stock-outs or excess inventory at some locations, resulting in the need to transfer inventory mid-week. While store managers may be able to make one-off stocking decisions based on weather conditions using their intuition and judgment, making buying, inventory placement, and workforce management decisions at scale becomes more challenging. Day-to-day weather variation also impacts hyper-local on-demand services that rely on efficient matching of supply and demand at scale. Programmatically applying local weather information at scale can help these customers preemptively match supply and demand.

Predicting future weather conditions is common, and while it is possible to use these predictions to more accurately forecast demand for products and services, customers struggle to do so in practice. Acquiring your own historical weather data and weather forecasts is expensive, and requires constant data collation, aggregation, and cleaning. Additionally, without weather domain expertise, transforming raw weather metrics into predictive data is challenging. With today’s launch, customers will be able to account for local day-to-day weather changes to better predict demand, with only one click and no additional costs, using Amazon Forecast. When you select the Weather Index, Forecast trains a model with historical weather information for the locations of your operations and uses the latest 14-day weather forecasts on items that are influenced by day-to-day variations to create more accurate demand forecasts.

To get started with this capability, see the details in our blog published on our resources page and go through the notebook in our GitHub repo that walks you through how to use the Forecast APIs to enable the Weather Index. You can use this capability in all Regions where Forecast is publicly available. For more information about Region availability, see Region Table.