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Using AI and Time Series Machine Learning Methods to Improve Demand Forecasting Models
Thursday, September 6, 2018
1:00 pm ET - 60 minutes with Q&A
Register for the webinar


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 webinar, DataRobot’s Chief Scientist Michael Schmidt and General Manager of Time Series Jay Schruen will explain time series analysis and its real-world use cases with a focus on retail demand forecasting methods.

The webinar will conclude 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 facing companies 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






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


DataRobot, Inc, One International Place, 5th Floor, Boston, MA 02110

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