Generate cold start forecasts for products with no historical data using Amazon Forecast, now up to 45% more accurate

Now with Amazon Forecast, you can generate up to 45% more accurate forecasts for products with no historical data. Forecast is a managed service that uses machine learning (ML) to generate accurate demand forecasts, without requiring any ML experience. Accurate forecasting is the foundation for inventory optimization, logistics planning, and workforce management and it enables businesses to be better prepared to serve their customers. Cold start forecasting is a common challenge where there is a need to generate a forecast but there is no historical data for the product. This is typical in industries such as retail, manufacturing, or consumer packaged goods where there is rapid new product introductions by bringing newly developed products to market, onboarding brands or catalogs for the very first time, or cross-selling products into new regions. With this launch, we improved on our existing approach to cold start forecasting and now provide forecasts that are up to 45% more accurate.

It can be challenging to develop a cold start forecasting model because traditional statistical forecasting methods such as Autoregressive Integrated Moving Average (ARIMA) or Exponential Smoothing are built using the concept that a product’s historical data can be used to predict its future values. But, without historical data, the model parameters can’t be calculated and thus the model can’t be built. Forecast already had the ability to generate forecasts for cold start products using proprietary neural network algorithms such as DeepAR+ and CNN-QR. These models learn relationships between products and can generate forecasts for products with no historical data. The usage of item metadata to establish these relationships was implicit which meant that the networks were not able to fully extrapolate trend characteristics for cold start products.

Today, we launched a new approach for cold start forecasting that is up to 45% more accurate than before. This approach improves our treatment of item metadata through which we identify explicit products within your dataset that have the most similar characteristics to the cold start products. By focusing on this subset of similar products, we are able to better learn trends to generate a forecast for the cold start product. For example, a fashion retailer introducing a new T-shirt line will want to forecast demand for that line to optimize store inventory. You can provide Forecast with historical data for other products in your catalog such as existing T-shirt lines, jackets, trousers, and shoes, as well as item metadata such as brand name, color, size, and product category for both new and existing products. With this metadata, Forecast automatically detects the products that are most closely related to the new T-shirt line and uses those to generate forecasts for the T-shirt line.

This feature is available in all Regions where Forecast is publicly available through the AWS Management Console or the AutoPredictor API. For more information about Region availability, see AWS Regional Services. To get started on using Forecast for cold start forecasting, refer to Generating Forecasts or the GitHub notebook.

Solution overview

The steps in this post demonstrate how to use Forecast for cold start forecasting on the AWS Management Console. We walk through an example of a retailer generating an inventory demand forecast for a newly launched product by following the three steps in Forecast: importing your data, training a predictor, and creating a forecast. To directly use the Forecast API for cold start forecasting, follow the notebook in our GitHub repo, which provides an analogous demonstration.

Import your training data

To use the new cold start forecasting method, you must import two CSV files: one file containing the target time series data (showing the prediction target), and another file containing the item metadata (showing product characteristics such as size or color). Forecast identifies cold start products as those products that are present in the item metadata file but aren’t present in the target time series file.

To correctly identify your cold start product, ensure that the item ID of your cold start product is entered as a row in your item metadata file and that it’s not contained in the target time series file. For multiple cold start products, enter each product item ID as a separate row in the item metadata file. If you don’t yet have an item ID for your cold start product, you can use any alphanumeric combination less than 64 characters that isn’t already representative of another product in your dataset.

In our example, the target time series file contains the product item ID, timestamp, and demand (inventory), and the item metadata file contains the product item ID, color, product category, and location.

To import your data, complete the following steps:

On the Forecast console, choose View dataset groups.

Choose Create dataset group.

For Dataset group name, enter a dataset name (for this post, my_company_shoe_inventory).
For Forecasting domain, choose a forecasting domain (for this post, Retail).
Choose Next.

On the Create target time series dataset page, provide the dataset name, frequency of your data, and data schema.
Provide the dataset import details.
Choose Start.

The following screenshot shows the information for the target time series page filled out for our example.

You’re redirected to the dashboard that you can use to track progress.

To import the item metadata file, on the dashboard, choose Import.

On the Create item metadata dataset page, provide the dataset name and data schema.
Provide the dataset import details.
Choose Start.

The following screenshot shows the information filled out for our example.

Train a predictor

Next, we train a predictor.

On the dashboard, choose Train predictor.

On the Train predictor page, enter a name for your predictor, how long in the future you want to forecast and at what frequency, and the number of quantiles you want to forecast for.
Enable AutoPredictor. This is required for cold start forecasting.
Choose Create.

The following screenshot shows the information filled out for our example.

Create a forecast

After our predictor is trained (this can take approximately 2.5 hours), we create a forecast for the newly launched product. You will know that your predictor is trained when you see the View Predictors button on your dashboard.

Choose Create a forecast on the dashboard.

On the Create a forecast page, enter a forecast name, choose the predictor that you created, and specify the forecast quantiles (optional) and the items to generate a forecast for.
Choose Start.

Export your forecasts

After your forecast is created, you can export the data to CSV. You will know that your forecast is created when you see the status is active.

Choose Create forecast export.

Enter the export file name (for this post, my_cold_start_forecast_export).
For Export location, specify the Amazon Simple Storage Service (Amazon S3) location.
Choose Start.

To download the export, navigate to the S3 file path location from the console, then select the file and choose Download.

The export file contains the timestamp, item ID, item metadata, and the forecasts for each quantile selected.

View your forecasts

After your forecast is created, you can view the forecasts for the new products graphically on the console.

Choose Query forecast on the dashboard.

Choose the name of the forecast created in the previous step (my_cold_start_forecast in our example).
Enter the start date and end date you want to view your forecast over.
In the item ID field for the forecast key, add the unique ID of your cold start product.
Chose Get forecast.

In the figure, you will see the forecast for any quantile selected.

Conclusion

With Forecast, you’re able to obtain the same forecasting insights for cold-start products with no historical data, now up to 45% more accurate than before. To generate cold start forecasts with Forecast, open the Forecast console and follow the steps outlined in this post, or refer to our GitHub notebook on how to access the functionality via API. To learn more, refer to Generating Forecasts.

About the authors

Brandon Nair is a Senior Product Manager for Amazon Forecast. His professional interest lies in creating scalable machine learning services and applications. Outside of work he can be found exploring national parks, perfecting his golf swing or planning an adventure trip.

Manas Dadarkar is a Software Development Manager owning the engineering of the Amazon Forecast service. He is passionate about the applications of machine learning and making ML technologies easily available for everyone to adopt and deploy to production. Outside of work, he has multiple interests including travelling, reading and spending time with friends and family.

Bharat Nandamuri is a Sr Software Engineer working on Amazon Forecast. He is passionate about building high scale backend services with focus on Engineering for ML systems. Outside of work, he enjoys playing chess, hiking and watching movies.

Gaurav Gupta is an Applied Scientist at AWS AI labs and Amazon Forecast. His research interests lie in machine learning for sequential data, operator learning for partial differential equations, wavelets. He completed his PhD from University of Southern California before joining AWS.