Using AI and Time Series Models to Improve Demand Forecasting

On Demand

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 session, 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

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