Real-time Spatial and Temporal Forecasting [Lyft]
Episode 117 of the Snacks Weekly on Data Science podcast, hosted by Pan Wu, titled "Real-time Spatial and Temporal Forecasting [Lyft]" was published on December 22, 2025 and runs 11 minutes.
December 22, 2025 ·11m · Snacks Weekly on Data Science
Summary
In this episode, we explore how Lyft identified the right algorithmic approach for building a real-time spatial-temporal forecasting system. The team evaluated two major model families for this task: classical time-series models and deep neural networks. This study highlights the balance between accuracy and practicality—and serves as a valuable guide for choosing machine learning solutions that truly meet business needs.For more details, you can refer to their published tech blog, linked here for your reference: https://eng.lyft.com/real-time-spatial-temporal-forecasting-lyft-fa90b3f3ec24
Episode Description
In this episode, we explore how Lyft identified the right algorithmic approach for building a real-time spatial-temporal forecasting system. The team evaluated two major model families for this task: classical time-series models and deep neural networks. This study highlights the balance between accuracy and practicality—and serves as a valuable guide for choosing machine learning solutions that truly meet business needs.
For more details, you can refer to their published tech blog, linked here for your reference: https://eng.lyft.com/real-time-spatial-temporal-forecasting-lyft-fa90b3f3ec24
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