Explain the features of Amazon Forecast

In this recipe, we will learn about Amazon Forecast. We will also learn about the features of Amazon Forecast.

Recipe Objective - Explain the features of Amazon Forecast?

The Amazon Forecast is a widely used service and is defined as a fully managed service that delivers extremely accurate forecasts using machine learning. Amazon Forecast, which is based on the same technology as Amazon.com, combines time series data with extra variables to create forecasts using machine learning. To get started with Forecast, Users don't need any machine learning skills. Users simply need to supply previous data, as well as any other information they think would affect their estimates. For example, demand for a specific colour of the garment may fluctuate depending on the season and retail location. This complicated link is difficult to discern on its own, but machine learning is well-suited to do so. Many of the domains that naturally create time-series data have forecasting issues. Retail sales, medical analysis, capacity planning, sensor network monitoring, financial analysis, social activity mining, and database systems are just a few of the examples. Forecasting, for example, is critical to automating and improving operational processes in most firms so that data-driven decision making is possible. Forecasts for product supply and demand may be used for inventory management, personnel scheduling, and topology planning, among other things, and are a critical tool for most elements of supply chain optimization.

Apply Machine Learning to Demand Forecasting Data Science Problems

Benefits of Amazon Forecast

  • The Amazon Forecast Amazon Forecast utilises machine learning (ML) to provide more accurate demand estimates with only a few clicks, and no prior ML training is required. Amazon Forecast comprises algorithms based on Amazon.com's twenty-year forecasting experience and built knowledge, delivering the same technology to developers as a fully managed service, eliminating the need to manage resources. Amazon Forecast utilises machine learning to learn not just the best algorithm for each item, but also the best ensemble of algorithms for each item, resulting in the best model for user's data being created automatically and thus providing advanced automated machine learning. Amazon Forecast includes a forecast Explainability report in the form of affect ratings for all the users' forecasts, particular periods of interest, or specified time durations, allowing users to see what elements, such as pricing, vacations, or weather, are driving its projections. Explainability gives you additional information about how to better manage a user's company's operations and thus it forecast explainability.

System Requirements

  • Any Operating System(Mac, Windows, Linux)

This recipe explains Amazon Forecast and the Features of Amazon Forecast.

Features of Amazon Forecast

    • It automatically includes the local weather information

The Amazon Forecast can improve your forecasting accuracy by automatically incorporating local weather information in users' demand predictions with one click and at no additional cost using Weather Index. Consumer demand patterns, product merchandising decisions, personnel requirements, and energy consumption requirements are all influenced by weather conditions. When users utilise the Weather Index, Forecast creates more accurate demand projections by training a model with historical weather data for the locations of your operations and using the most recent 14-day weather forecasts for goods that are impacted by day-to-day fluctuations.

    • It generates probabilistic forecasts

Unlike most other forecasting tools, Amazon Forecast creates probabilistic projections at three distinct quantiles by default: 10%, 50%, and 90%. Additionally, you have the option of selecting any quantile between 1% and 99 per cent, including the mean estimate. This enables users to select a prediction that best meets the company's needs, based on whether the cost of capital (over forecasting) or missing client demand (under forecasting) is a priority.

    • It works with any historical time series data to further create accurate forecasts

To develop reliable projections for your business, Amazon Forecast can leverage nearly any historical time series data (e.g., pricing, promotions, economic performance measures). In a retail context, for example, Amazon Forecast processes time-series data (such as pricing, promotions, and shop traffic) and integrates it with associated data (such as product attributes, floor layout, and store locations) to discover the complicated correlations between them. Amazon Forecast can be 50% more accurate than non-machine learning forecasting solutions by mixing time series data with extra factors.

    • It helps in evaluating the accuracy of the forecasting models

Amazon Forecast provides six different comprehensive accuracy metrics to help users understand the performance of their forecasting model and compare it to previous forecasting models you've created, which may have looked at a different set of variables or used historical data from a different period. Amazon Forecast divides the data into a training and testing set, allowing users to download the forecasts it generates for the testing set and use a custom metric to evaluate the accuracy, or create multiple backtest windows and visualise the metrics, allowing users to evaluate model accuracy over multiple start dates.

What Users are saying..

profile image

Ed Godalle

Director Data Analytics at EY / EY Tech
linkedin profile url

I am the Director of Data Analytics with over 10+ years of IT experience. I have a background in SQL, Python, and Big Data working with Accenture, IBM, and Infosys. I am looking to enhance my skills... Read More

Relevant Projects

Web Server Log Processing using Hadoop in Azure
In this big data project, you will use Hadoop, Flume, Spark and Hive to process the Web Server logs dataset to glean more insights on the log data.

Create A Data Pipeline based on Messaging Using PySpark Hive
In this PySpark project, you will simulate a complex real-world data pipeline based on messaging. This project is deployed using the following tech stack - NiFi, PySpark, Hive, HDFS, Kafka, Airflow, Tableau and AWS QuickSight.

Retail Analytics Project Example using Sqoop, HDFS, and Hive
This Project gives a detailed explanation of How Data Analytics can be used in the Retail Industry, using technologies like Sqoop, HDFS, and Hive.

PySpark Tutorial - Learn to use Apache Spark with Python
PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.

Learn Real-Time Data Ingestion with Azure Purview
In this Microsoft Azure project, you will learn data ingestion and preparation for Azure Purview.

SQL Project for Data Analysis using Oracle Database-Part 3
In this SQL Project for Data Analysis, you will learn to efficiently write sub-queries and analyse data using various SQL functions and operators.

Learn Efficient Multi-Source Data Processing with Talend ETL
In this Talend ETL Project , you will create a multi-source ETL Pipeline to load data from multiple sources such as MySQL Database, Azure Database, and API to Snowflake cloud using Talend Jobs.

Build a Real-Time Dashboard with Spark, Grafana, and InfluxDB
Use Spark , Grafana, and InfluxDB to build a real-time e-commerce users analytics dashboard by consuming different events such as user clicks, orders, demographics

COVID-19 Data Analysis Project using Python and AWS Stack
COVID-19 Data Analysis Project using Python and AWS to build an automated data pipeline that processes COVID-19 data from Johns Hopkins University and generates interactive dashboards to provide insights into the pandemic for public health officials, researchers, and the general public.

PySpark ETL Project for Real-Time Data Processing
In this PySpark ETL Project, you will learn to build a data pipeline and perform ETL operations for Real-Time Data Processing