Creating Analyses with Demand Data

This notebook is designed to provide you with the context you need to get started with the Beam API and use it effectively.

Beam is PredictHQ's automated correlation engine to accurately reveal the events that drive demand for your business. As well as showing you the correlation between events and your demand data. Beam can also decompose your demand data which can help improve your demand forecasting accuracy. For more information on Beam see the Beam Overview.

Our goal is to assist users in bulk uploading multiple demand datasets to Beam, allowing them to receive decomposed demand data on a large scale. With the bulk upload feature, users can create multiple analyses at once from their source data. On the other hand, the decomposed data feature allows users to extract decomposed results using the Beam API after uploading their data.

Utilising the decomposition of your demand data can enhance the accuracy of your forecasts. If you currently do not decompose your data for forecasting purposes, you can leverage Beam's decomposition functionality to obtain a breakdown of your data. Beam's decomposition process separates your data into baseline demand and remainder components. Improved decomposition data can lead to enhanced forecast accuracy.

It has the following three parts:

  1. Uploading location and demand data to Beam.

  2. Generating correlation results

  3. Plotting and interpretation

You can find the notebook on Github.