Amazon Forecast is a fully managed service that uses machine learning to deliver highly accurate forecasts without the need to provision servers, and no machine learning models to build, train, or deploy. You pay only for what you use, and there are no minimum fees and no upfront commitments.
Based on the same technology used at Amazon.com, Amazon Forecast uses machine learning to combine time series data with additional variables to build forecasts. Amazon Forecast requires no machine learning experience to get started. You only need to provide historical data, plus any additional data that you believe may impact your forecasts. For example, the demand for a particular color of a shirt may change with the seasons and store location. This complex relationship is hard to determine on its own, but machine learning is ideally suited to recognize it. Once you provide your data, Amazon Forecast will automatically examine it, identify what is meaningful, and produce a forecasting model capable of making predictions that are up to 50% more accurate than looking at time series data alone.
- 50% more accurate forecasts using machine learning to automatically discover how time series data & other variables like product features and store locations affect each other
- Reduce forecasting time from months to hours
- Create virtually any time series forecast for every industry and use case, including retail, logistics, finance, advertising performance and many more.
- Any content processed by Amazon Forecast is encrypted, ensuring that sensitive information is kept secure and confidential.
Product Demand Planning
Amazon Forecast can be use to forecast inventory for your various store locations.
Accurate financial forecasting like sales revenue predictions is fundamental to every busines’ success. Amazon Forecast can forecast key financial metrics such as revenue, expenses, and cash flow across multiple time periods and monetary units.
Planning for the right level of available resources such as staffing levels, advertising inventory and raw materials for control costs.