AWS Announces General Availability of Amazon Forecast

Today, Amazon Web Services, Inc. (AWS), an Amazon.com company (NASDAQ: AMZN), announced the general availability of Amazon Forecast, a fully managed service that uses machine learning to deliver highly accurate forecasts based on the same technology that powers Amazon.com. Amazon uses forecasting to make sure that the right product is in the right place at the right time by predicting demand for hundreds of millions of products every day. Amazon Forecast uses this same technology to build precise forecasts for virtually any business condition, including product demand and sales, infrastructure requirements, energy needs, and staffing levels – with predictions that are up to 50% more accurate than traditional methods. Amazon Forecast is easy to use and requires no machine learning experience. The service automatically provisions the necessary infrastructure, processes data, and builds custom, private machine learning models that are hosted on AWS and ready to make predictions. To get started with Amazon Forecast, visit https://aws.amazon.com/forecast/.

Forecasting is the science of predicting the future. By examining historical trends, organizations can make a call on what might happen and when, and build that into their future plans for everything from product demand to inventory to staffing. Given the consequences of forecasting, accuracy really matters. If a forecast is too high, customers will over-invest in products and staff, which ends up as wasted investment, and if the forecast is too low, they will under-invest, which leads to a shortfall in raw materials and inventory; creating a poor customer experience. Today, companies try to use everything from simple spreadsheets to complex financial planning software to generate forecasts, but high accuracy remains elusive for two reasons. First, traditional forecasts struggle to incorporate very large volumes of historical data, missing out on important signals from the past that are lost in the noise. Second, traditional forecasts rarely incorporate related but independent data, which can offer important context (such as sales, holidays, locations, marketing promotions, etc.). Without the full history and the broader context, most forecasts fail to predict the future accurately.