Across many industries, we are helping transform companies to embrace the benefits of Artificial Intelligence and Machine Learning for concrete business use cases.
Amazon Forecast provides forecasts that are up to 50% more accurate by using machine learning to automatically discover how time series data and other variables like product features and store locations affect each other. You are better able to understand how these complex relationships ultimately affect demand than what looking at time series data alone can deliver. The models that Amazon Forecast builds are unique to your data, which means the predictions are custom fit to your business.
With Amazon Forecast, you can achieve forecasting accuracy levels that used to take months of engineering in as little as a few hours. You can import time series data and associated data into Amazon Forecast from your Amazon S3 database. From there, Amazon Forecast automatically loads your data, inspects it, and identifies the key attributes needed for forecasting. Amazon Forecast then trains and optimizes your custom model, and hosts them in a highly available environment where it can be used to generate your business forecasts. By automatically handling the complex machine learning required to build, train, tune, and deploy a forecasting model, Amazon Forecast enables you to create accurate forecasts quickly.
Multiple types of time series forecasts are required to run your business, from cash flow to product demand to resource planning. Amazon Forecast allows you to build forecasts for virtually every industry and use case, including retail, logistics, finance, advertising performance, and many more. Using machine learning, Amazon Forecast can work with any historical time series data and use a large library of built-in algorithms to determine the best fit for your particular forecast type automatically.
Every interaction you have with Amazon Forecast is protected by encryption. Any content processed by Amazon Forecast is encrypted with customer keys through Amazon Key Management Service, and encrypted at rest in the AWS Region where you are using the service. Administrators can also control access to Amazon Forecast through an AWS Identity and Access Management (IAM) permissions policy – ensuring that sensitive information is kept secure and confidential.
You can use Amazon Forecast to forecast the appropriate inventory levels for your various store locations. You provide forecast information like historical sales, pricing, store promotions, store locations, and catalog data from your retail management systems in a CSV (comma-separated values) format into Amazon S3 storage. You can then combine that with associated data like website traffic logs, weather, and shipping schedules. Amazon Forecast will use that information to produce a model that can accurately forecast customer demand for products at the individual store level. Export your forecasts in batch in CSV format and import them back into your retail management systems so that you can determine how much inventory to purchase and allocate per store.
Accurate financial forecasting like sales revenue predictions is fundamental to every business’ success. Amazon Forecast can forecast key financial metrics such as revenue, expenses, and cash flow across multiple time periods and monetary units. You first upload your historical financial time series data to Amazon S3 storage and then import it to Amazon Forecast. After producing a model, Amazon Forecast will provide you with the expected accuracy of the forecast so that you can determine if more data is required before using the model in production. The service can also visualize forecasts with graphs in the Amazon Forecast Console to help you make informed decisions.
Planning for the right level of available resources, such as staffing levels, advertising inventory, and raw material for manufacturing is important to maximize revenue and control costs. For example, a broadcasting company may want to optimize ad inventory regionally. It can import historical viewership data across different program categories and geographic regions, content metadata, and regional demographics into Amazon Forecast. The service will learn from this data and provide accurate local forecasts.
Amazon Forecast can use virtually any historical time series data (e.g., price, promotions, economic performance metrics) to create accurate forecasts for your business. For example, in a retail scenario, Amazon Forecast uses machine learning to process your time series data (such as price, promotions, and store traffic) and combines that with associated data (such as product features, floor placement, and store locations) to determine the complex relationships between them. By combining time series data with additional variables, Amazon Forecast can be 50% more accurate than non-machine learning forecasting tools.
No machine learning expertise is required to build an accurate time series-forecasting model that can incorporate time series data from multiple variables at once. Amazon Forecast includes Auto ML capabilities that take care of the machine learning for you. Once you provide your data into Amazon S3, Amazon Forecast can automatically load and inspect the data, select the right algorithms, train a model, provide accuracy metrics, and generate forecasts.
Amazon Forecast provides comprehensive accuracy metrics to help you understand the performance of your forecasting model and compare it to previous forecasting models you’ve created that may have looked at a different set of variables or used a different period of time for the historical data. Amazon Forecast allows you to create multiple back test windows and visualize the metrics, helping you evaluate model accuracy over different start dates.
A fully managed service by AWS, Amazon Forecast uses Machine Learning to predict future business conditions and time-series data. It automatically discovers patterns in historical data, generates predictions, and continuously improves demand forecasting over time using algorithms.
Amazon Forecast analyzes historical input data to produce forecasting by using advanced machine learning capabilities. The way this works is that historical raw data is initially imported into Amazon Forecast. It then applies data reprocessing, algorithmic selection, and feature engineering automatically to train multiple machine learning models. This process compares performance to select the best model. Accurate demand forecasts are then generated for future time periods after the model is trained.
Amazon Forecast supports a wide range of data, including but not limited to –
• Time series data
• Categorical data
• Metadata
Time series data represents measurements or observations taken over time, such as –
• Sales data
• Stock prices
• Weather data
Categorical data includes qualitative information describing characteristics of time series such as –
• Product categories
• Geographical regions
Metadata provides additional information to boost the accurate demand forecasts, such as –
• Holidays
• Marketing promotions
• Special events
Yes, it can. Its scaling features accommodate nuances of sizes in datasets and can handle billions of data points. Further, its use of distributed computing resources allows to train models efficiently and helps promptly process large volumes of data.
Users with varying levels of machine learning expertise can use this service. Automation tools and a user-friendly interface are in-built into this service, simplifying the whole process. Users can leverage pre-built algorithms and default configurations to generate forecasts quickly, or they can customize the models and fine-tune the parameters to meet specific requirements.
The costs incurred in leveraging Amazon Forecast vary depending on the amount of data stored and processed and on the forecasts generated. It is always a great idea to check the prices directly with AWS, as they may vary over time.
Yes. You can use services such as –
• AWS Lambda to automate data import
• AWS CloudWatch for monitoring
• AWS S3 for storing inputted data and exporting forecasts
• AWS IAM for managing access to the service
There is no fixed limit to forecast. Amazon Forecast supports both short-term and long-term forecasting. The forecast horizon can vary depending on the specific use case and the nature of the data. For long-term forecasts, it is essential to consider the availability and quality of historical data. For short-term forecasts, you can typically generate predictions for several time steps ahead, such as hours, days, or weeks.
