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.
- 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