Enabling the AI-Driven Enterprise

Using AI and Time Series Models to Improve Demand Forecasting

Smidt Schuren

Time series machine learning models allow organizations to predict future values based on past and present data. Translation: companies can use time series to solve critical problems such as optimizing staffing levels, managing inventory, forecasting future product demand, and more.

 

In this on-demand webinar, DataRobot’s Chief Scientist Michael Schmidt and General Manager of Time Series Jay Schuren explain time series analysis and its real-world use cases with a focus on retail demand forecasting methods. The webinar concludes with a demo of DataRobot Time Series, which makes it possible for organizations to automatically develop highly accurate time series models without programming knowledge.

You’ll discover:

  • Why time series analysis is critical to any organization dealing with time-sensitive operations
  • Key challenges companies face when looking to develop traditional time series machine learning models
  • Time series use cases and success stories
  • A demo of DataRobot Time Series showcasing how the product automates time series feature engineering, problem setup, backtesting, and modeling to achieve the highest possible accuracy

Presenters

Michael Schmidt
Michael Schmidt
Chief Scientist, DataRobot
Jay Schuren
Jay Schuren
Data Scientist and General Manager of Time Series, DataRobot