Amazon Forecast now uses Convolutional Neural Networks (CNNs) to train forecasting models up to 2X faster with up to 30% higher accuracy
Amazon Forecast uses machine learning to generate accurate demand forecasts, without requiring any prior ML experience for inventory planning, workforce planning, energy demand forecasting and cloud infrastructure usage forecasting. This technology has been developed from over 20 years of forecasting at Amazon.com. Amazon Forecast is a fully managed service, so there are no servers to provision, and no machine learning models to build, train, or deploy.
Amazon Forecast uses machine learning to generate accurate demand forecasts, without requiring any prior ML experience for inventory planning, workforce planning, energy demand forecasting and cloud infrastructure usage forecasting. This technology has been developed from over 20 years of forecasting at Amazon.com. Amazon Forecast is a fully managed service, so there are no servers to provision, and no machine learning models to build, train, or deploy.
We’re excited to announce that Amazon Forecast can now use Convolutional Neural Networks (CNNs) to train forecasting models up to 2X faster with up to 30% higher accuracy. CNN algorithms are a class of neural network-based machine learning (ML) algorithms that play a vital role in Amazon.com’s demand forecasting system and enable Amazon.com to predict demand for over 400 million products every day. For more information about Amazon.com’s journey building demand forecasting technology using CNN models, watch the re:MARS 2019 keynote video. Forecast brings the same technology used at Amazon.com into the hands of everyday developers as a fully managed service. Anyone can start using Forecast, without any prior ML experience, by using the Forecast console or the API.
At Amazon, we have learned over the years that no one algorithm delivers the most accurate forecast for all types of data. Traditional statistical models have been useful in predicting demand for products that have regular demand patterns, such as sunscreen lotions in the summer and woolen clothes in the winter. However, statistical models can’t deliver accurate forecasts for more complex scenarios, such as frequent price changes, differences between regional versus national demand, products with different selling velocities, and the addition of new products. Sophisticated deep learning models can provide higher accuracy in these use cases. Forecast automatically examines your data and selects the best algorithm across a set of statistical and deep learning algorithms to train the more accurate forecasting model for your data. With the addition of the CNN-based deep learning algorithm, Forecast can now further improve accuracy by up to 30% and train models up to 2X faster compared to the currently supported algorithms. This new algorithm can more accurately detect leading indicators of demand, such as pre-order information, product page visits, price changes, and promotional spikes, to build more accurate forecasts.
To get started, learn more about how to use the CNN algorithm in our blog and see the CNN-QR algorithm documentation. The new CNN algorithm is available in all Regions where Forecast is publicly available. For more information about Region availability, see Region Table.