Accurate time-series forecasting service, based on the same technology used at Amazon.com, no machine learning experience required
Amazon Forecast is a fully managed service that uses machine learning to deliver highly accurate forecasts.
Companies today use everything from simple spreadsheets to complex financial planning software to attempt to accurately forecast future business outcomes such as product demand, resource needs, or financial performance. These tools build forecasts by looking at a historical series of data, which is called time series data. For example, such tools may try to predict the future sales of a raincoat by looking only at its previous sales data with the underlying assumption that the future is determined by the past. This approach can struggle to produce accurate forecasts for large sets of data that have irregular trends. Also, it fails to easily combine data series that change over time (such as price, discounts, web traffic, and number of employees) with relevant independent variables like product features and store locations.
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.
Amazon Forecast is a fully managed service, so there are no servers to provision, 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.
50% more accurate forecasts with machine learning
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.
Reduce forecasting time from months to hours
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.
Create virtually any time series forecast
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.
Secure your business data and peace of mind
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.
How it works
Product Demand Planning
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.
CasaOne offers a one-stop, cost-effective furniture rental/leasing solution with design guidance, seamless project management and a white-glove moving, delivery, and installation experience.
“At CasaOne, we make sure our customers get access to their furniture in a few business days. In order to better predict how many couches CasaOne customers might rent in the Bay Area or how many coffee tables customers might rent in NY, we leverage the capabilities of Amazon Forecast. With Amazon Forecast, our sales prediction accuracy has improved by 20% compared to our existing forecasting algorithm. This means we can stock the right products and save on purchase costs worth a few thousand dollars. Improved product selection will also lead to enhanced customer experience in the long run."
Madhusudan Kagwad, Co-founder and Head of Products - CasaOne
OMOTOR helps businesses improve through AI by providing them with the best of machine learning algorithms, computer vision techniques and cognitive bots that can communicate via WhatsApp and others platforms.
"At OMOTOR, we use AI to innovate on behalf of our customers, so access to the most cutting-edge deep learning technologies from AWS is imperative to our client's success. Using Amazon Forecast gives us the ability to create and refine various forecasts from time series data without having to build and train a model manually every time. We forecast real sales for the next 12 months, so we can adequately plan for inventory, estimate future profitability, track market share gain or loss, and other insights. This means we can use more contextual data, optimize more frequently, generate forecasts with upwards of 50% improvements in accuracy, and operate at a great speed. For example, we're helping customers in the automotive industry predict sales across 185 vehicles in Brazil.”
Marcio Rodrigues, CEO - OMOTOR
OMNYS provides ground-breaking solutions, by designing and building Digital Platforms based on System Integration, Web & Mobile Technologies, IoT, Machine Learning and Big Data. Throughout learning of ultimate technologies, R&D, analysis of market needs OMNYS brings innovation to many industries.
“Amazon Forecast is helping us bring new insights and business value for our client Arneg S.p.A., a global leader in refrigerator manufacturing collecting about 11 million IoT records daily. Using Amazon Forecast, within hours we were able to start building models that would have traditionally taken weeks or months. Our client simply wanted us to extract value from their raw data and with Amazon Forecast we were able to do much more. We built up models to predict energy consumption three days in advance for their refrigerators installed in malls around the world, with a 91% accuracy, and maintenance predictive models to better assess the risk of devices outage, at least up to one day in advance, and consequently reducing the number of emergency calls from their clients. The potential for our client to take these insights to improve how they manage their customer experience is limitless."
Davide Pozza, CTO - OMNYS
Puget Sound Energy
Puget Sound Energy (PSE) is the state’s largest utility, supporting 1.1 million electric customers and 825,000 natural gas customers in communities in 10 Washington counties.
“At PSE, we’ve used Amazon Forecast to forecast electric and gas consumption at a typical residence. We found that even with a very limited set of historical consumption and weather data, Amazon Forecast performed very well at forecasting 30 days out with virtually no manual effort. With the increased emphasis on environmentally-friendly energy solutions, the ability to produce more accurate energy usage projections at each of our customers’ homes and businesses will be essential for energy service providers like PSE. With these enhanced analytical capabilities, PSE will be able to identify custom energy saving programs and services, ultimately reducing customer bills.”
Paul Johnson, Sr. Cloud Architect - PSE