Improving Time Series Models in DataRobot
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)
After watching the learning session, you should check out these resources for more information.
- DataRobot Public Documentation — Automated Time Series Modeling
- DataRobot Pathfinder (Use Case Library) — see this query for Time Series use cases
- DataRobot Webinar — Using AI and Time Series Models to Improve Demand Forecasting
- Blog — AI Simplified: Time Series