Note: AWS announced the deprecation of Amazon Forecast in 2024. The service reached end-of-life in 2025. This article is preserved for historical reference. For current AWS cost forecasting, see AWS Cost Explorer’s built-in forecasting capabilities.
To help companies achieve forecast accuracy, Amazon Web Services (AWS) offers a powerful tool that Amazon claims is 50% more accurate than traditional forecasting methods: Amazon Forecast.
In this article, AWS forecasting refers to using Amazon Forecast to analyze historical time-series data and estimate future trends, demand, and resource needs. Below, we cover what Amazon Forecast is, how its workflow operates, and the main limitations teams should understand before using it.
Key Takeaways
- AWS forecasting here refers to using Amazon Forecast to generate forecasts from historical time-series data.
- The workflow includes data import, preprocessing, domain selection, model training, evaluation, forecast generation, and export.
- Forecast quality depends heavily on the quality and completeness of the historical data used to train the model.
- Forecasting can improve AWS cost planning by helping teams estimate future resource needs more accurately.
What is AWS forecasting?
Amazon Forecast is a cloud-based AWS service that uses machine learning algorithms to generate accurate and reliable forecasts for time-series data.
Time-series forecasting allows companies to analyze historical data to generate forecasts or predictions for future trends and AWS resource needs. Amazon Forecast lets you automate this process.
When used with other AWS tools like Amazon Quicksight, Amazon Forecast can generate valuable insights businesses can use for AWS cost optimization.
How does AWS forecasting work?
To put things into context, let’s look at a brief breakdown of how AWS forecasting works:
1. Data import and preparation
The first step in generating forecasts is importing your data. Amazon Forecast supports structured data, including time-stamped events, target values, and related attributes. It can also import and organize data based on attributes like time, geography, and product category.
2. Data cleaning and preprocessing
Once you import data, it’s automatically cleaned and reprocessed to ensure accuracy and consistency. This involves fixing issues like missing values, outliers, and inconsistencies in a dataset.
Amazon Forecast’s data cleaning tools allow companies to transform raw data into a format more suitable for generating forecasts. This saves time and eliminates issues that could skew the forecast’s accuracy.
3. Forecasting domain selection
This is when you can define the forecast’s scope and context. It starts with selecting the domain you want to forecast, like retail demand, inventory planning, and Amazon EC2 capacity.
AWS forecasts also allow you to define a forecast horizon and set other parameters for your forecast. You can also create a forecast with Amazon Forecast using your Amazon Forecast console, AWS CLI, or SDK for Python (Boto3).
4. Algorithm selection and training
Next, you’ll need to select the appropriate algorithm and train it using historical data.
To create a predictor using Amazon Forecast, you have to train the algorithm using historical time-series data, which includes your observations/events, timestamps, and the target field you want to predict.
Amazon Forecast also lets you create datasets not included in the target time series dataset and use them to improve the accuracy of your predictor.
5. Model evaluation
Amazon Forecast provides metrics and visualizations that make it easy to evaluate the reliability of the models you create. This aids in fine-tuning the models and selecting the most appropriate algorithm for your specific forecasting task.
6. Forecast generation
You can use Amazon Forecast to automatically generate forecasts for future periods. These forecasts provide valuable insights into potential trends, empowering companies to plan effectively for future scenarios.
7. Exporting forecasts
Amazon Forecast also includes APIs and other export options that make incorporating forecast data into other applications easy.
When you export a forecast using Amazon Forecast, AWS saves the data to your Amazon S3 bucket as a CSV file by default. You also have the option to specify the file format you want to use.
Key features and benefits of AWS forecasting:
Some of the top features and benefits of Amazon Forecast include:
Automatic data preparation
Traditional data preparation processes can be time-consuming and complex, involving tedious tasks like cleaning, transforming, and structuring data. Amazon Forecast streamlines this process by automating data preparation tasks.
AWS Forecast takes raw, unprocessed data and uses machine learning techniques to fix missing values, outliers, and other data issues. This ensures the dataset will serve as a reliable source of information for the forecasting model you create.
Forecasting algorithms
From demand forecasting for retail to financial predictions to supply chain optimization, Amazon Forecast provides algorithms for multiple use cases. These algorithms can also be fine-tuned and adjusted in many ways to better suit the task at hand.
Optimizing inventory and reducing waste
Accurate forecasts are vital for inventory management and waste reduction. Without accurate forecasts, predicting consumer demand and its trend factors (i.e., seasonality, product pricing, market conditions) can be a real challenge.
But with Amazon Forecast, companies can accurately predict inventory needs to avoid overstocking and understocking products—saving money, time, and storage.
Scalability
Amazon Forecast is a highly scalable solution capable of handling large datasets and scaling according to business requirements. This ensures companies can continue generating accurate forecasts as their business grows and their operations expand.
Limitations of AWS forecasting
Amazon Forecast is a powerful tool, but does have its limitations:
Data requirements
Because Amazon Forecast relies on historical data to train forecast models, the accuracy of its generated forecasts heavily depends on the quality, completeness, and relevance of that historical data.
Additionally, the need for a substantial amount of historical data could pose challenges for new startups or companies entering new markets.
Lengthy training time
Training forecasting models can be intensive, especially when dealing with large datasets. Amazon Forecast provides scalable infrastructure designed to handle such tasks, but training time can still be lengthy and computationally demanding. This is especially true for complex models or models requiring large data volumes.
Limited customization
Amazon Forecast provides an impressive list of algorithms designed for various forecasting scenarios. However, your options for further customizing these algorithms are limited, which can present issues for companies with highly specialized forecasting needs.
Interpretability
Amazon Forecast focuses on accuracy and predictive power but doesn’t always provide detailed insights into why it made certain predictions. This lack of interpretability can make it difficult to understand the underlying factors driving a forecast.
How forecasting supports AWS cost optimization
One advantage of predicting metrics like website traffic and customer demand is improved AWS cost optimization. When you can accurately estimate your company’s future resource needs, everything from budgeting to choosing the right discount instruments (like Savings Plans and Reserved Instances) becomes much easier.
Forecasting helps estimate future resource needs, while ProsperOps manages discount instruments to act on that data more efficiently. This is where ProsperOps shines.
With ProsperOps, companies can automatically manage discount instruments without any manual monitoring or adjustments. This ensures that your AWS resources are always optimized for cost efficiency while freeing your engineering team up to focus on other tasks and cost-saving opportunities.
By using ProsperOps alongside powerful AWS tools like Amazon Forecast, you can automatically optimize your cloud computing workloads for maximum cost-efficiency in a way that doesn’t impact performance.
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FAQs
What is AWS forecasting?
AWS forecasting refers to using Amazon Forecast to generate forecasts from time-series data. Amazon Forecast is a cloud-based AWS service that uses machine learning algorithms to generate accurate and reliable forecasts for time-series data. Teams can use it to analyze historical data and estimate future trends and AWS resource needs.
How does Amazon Forecast work?
Amazon Forecast starts with data import and preparation, then moves through cleaning and preprocessing, domain selection, algorithm selection and training, model evaluation, forecast generation, and export. You can also create a forecast with Amazon Forecast using your Amazon Forecast console, AWS CLI, or SDK for Python (Boto3). The service also includes metrics, visualizations, APIs, and export options that help teams evaluate and use forecast outputs.
What data does Amazon Forecast need?
Amazon Forecast supports structured data, including time-stamped events, target values, and related attributes. Because Amazon Forecast relies on historical data to train forecast models, the accuracy of its generated forecasts heavily depends on the quality, completeness, and relevance of that historical data. Additionally, the need for a substantial amount of historical data could pose challenges for new startups or companies entering new markets.
How does forecasting support AWS cost optimization?
One advantage of predicting metrics like website traffic and customer demand is improved AWS cost optimization. When you can accurately estimate your company’s future resource needs, everything from budgeting to choosing the right discount instruments becomes much easier. Forecasting helps estimate future resource needs, while ProsperOps manages discount instruments to act on that data more efficiently.
