Find Machine Learning (ML) Features to use in Forecasting with Beam

Published on April 12, 2024

Find powerful Machine Learning (ML) features to boost your forecasting accuracy

Events are major catalysts of demand — and we find customers see significant improvements when updating their demand forecast to use events, with many customers getting 5-10% improvements in mean absolute percentage error (MAPE). Improved forecasting accuracy supports data-driven decision-making and enables more resilient and agile business strategies. 

To add events to your forecast, you need to implement machine learning (ML) features for events. We have found that creating new ML features can be time-consuming and expensive. It can take a significant amount of time for data scientists, data engineers, and others. PredictHQ provides a toolkit to allow you to easily integrate prebuilt, forecast-ready ML features with our Demand Analysis tool Beam and the Features API. We’ve made some updates to make this process even easier.

To do this quickly and easily, the first step is to upload your aggregated demand data into Beam in our web application Control Center or via the Beam API. Beam is our demand analysis tool which returns category importance results (also known as feature importance) for individual business locations as well as large groups.

Category importance uses our underlying Feature Importance API and is generated from an analysis of the relevance of the results of that API. Under the hood in Beam, we have a list of ML features and their p-values.

If you want to use the results to improve the accuracy of your models, you need to identify the ML features to use. We've added a link "View ML Features" in Beam for individual analyses and group analyses. This allows you to get a list of ML features to use to train your models. You can copy these features and use them in your code as described in our Get ML Features guide. The features are listed in order of the most impactful to least impactful (sorted by p-value). 


Here’s how to use Beam and the Features API for your forecast in five simple steps:

  1. Run Beam across your locations and create a group to get feature importance results to identify the top 5 most impactful features.

    1. In Control Center, open a Beam Analysis for your group and click on "VIEW ML FEATURES" to get a list of the most impactful features.

    2. Copy the ML features.

  2. Call the Features API (see the Get ML Features guide) to download historical event features to train your model across all your locations.

  3. Train the ML Model.

  4. Integrate calls to the Features API in your pipeline and pass values to the model for the future-facing period they are forecasting.

  5. Deploy the updated model to production.

If you want to see more details than what is returned by the "View ML Features" option in Control Center, you can call Get Feature Importance in the Beam API.

Questions? Let us know, we’d love to hear from you.