Improving Time Series Models in DataRobot

June 8, 2020
· 1 min read

This post was originally part of the DataRobot Community. Visit now to browse discussions and ask questions about the DataRobot AI Platform, data science, and more.

Time series forecasting is a critical component of any business when solving problems such as demand forecasting, staffing, inventory management, and more. In today’s world, leveraging automated machine learning for such use cases is paramount for maintaining a competitive edge. However, due to real-world complexities, additional strategies are often needed to achieve strong performance.

In this learning session, DataRobot’s Jess Lin and Taylor Larkin will discuss tips and tricks to improve time series models. Topics will include:

  • Problem framing for optimal results
  • Data preparation for increasing performance
  • Adjusting DataRobot project settings
  • Advanced time series blueprints
  • Best practices for model evaluation


  • Jess Lin (DataRobot, Data Scientist)
  • Taylor Larkin (DataRobot, Data Scientist)
  • Jack Jablonski (DataRobot, AI Success Manager)

Now what?

After watching the learning session, you should check out these resources for more information.

Next-Generation Time Series

Forecasting for the Real World, Not the Ideal World

Download Now
About the author
Linda Haviland
Linda Haviland

Community Manager

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