Features of Amazon Forecast
- The information about the local weather is automatically included: By leveraging Weather Index to instantly and cost-free automatically incorporate local weather data into customers’ demand projections, Amazon Forecast can increase forecasting accuracy. Weather conditions have an impact on labor needs, consumer demand patterns, product merchandising choices, and energy consumption needs. By analyzing the most recent 14-day weather forecasts for items that are affected by daily changes and training a model with historical weather data for the locations of your operations, Forecast improves demand projections when customers utilize the Weather Index.
- It creates forecasts that are probabilistic: Unlike the majority of other forecasting tools, Amazon Forecast by default generates probabilistic estimates at the 10%, 50%, and 90% quantiles. You also have the choice of selecting any quantile in the range of 1% to 99%, including the mean estimate. Users can now choose a prediction based on whether it is more important to save capital costs (over forecasting) or meet customer demand (under forecasting), depending on the needs of the business.
- It uses any previous time series data to produce more precise forecasts: Almost any historical time series data can be used by Amazon Forecast to create accurate estimates for your company (e.g., pricing, promotions, economic performance measures). To find the complex relationships between time-series data (such as pricing, promotions, and shop traffic) and associated data (like product attributes, floor plans, and store locations), Amazon Forecast evaluates time-series data in the context of retail. By combining time series data with additional parameters, Amazon Forecast can be up to 50% more accurate than non-machine learning forecasting systems.
- It aids in assessing the forecasting models’ accuracy: To help users evaluate the performance of their forecasting model and contrast it with earlier forecasting models you’ve created, which might have examined a different set of variables or used historical data from a different period, Amazon Forecast offers six different comprehensive accuracy metrics. The data is split into a training and testing set by Amazon Forecast, which enables users to download the forecasts it produces for the testing set and assess the accuracy using a custom metric. Users can also create multiple backtest windows and visualize the metrics to assess model accuracy across various start dates.
What is Amazon Forecast?
Pre-requisite: AWS
The frequently used Amazon Forecast is an example of a fully managed service that uses machine learning to produce incredibly accurate forecasts. Using machine learning, Amazon Forecast, which uses the same technology as Amazon.com, integrates time series data with other variables to provide forecasts. Users don’t need any prior knowledge of machine learning to start using Forecast. Users only need to provide prior data and any additional information they believe will alter their estimates. For instance, depending on the time of year and the retailer, demand for a particular color of clothing may change. On its own, it is challenging to identify this complex link, but machine learning is well adapted to do so. Forecasting problems exist in many of the fields that naturally produce time-series data. Just a few examples include database systems, retail sales, medical analysis, capacity planning, sensor network monitoring, financial analysis, and financial analysis.