<description>&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;For more details, you can refer to their published tech blog, linked here for your reference: &lt;a href="https://eng.lyft.com/real-time-spatial-temporal-forecasting-lyft-fa90b3f3ec24"&gt;&lt;u&gt;https://eng.lyft.com/real-time-spatial-temporal-forecasting-lyft-fa90b3f3ec24&lt;/u&gt;&lt;/a&gt;&lt;/p&gt;
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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: <a href="https://eng.lyft.com/real-time-spatial-temporal-forecasting-lyft-fa90b3f3ec24">https://eng.lyft.com/real-time-spatial-temporal-forecasting-lyft-fa90b3f3ec24</a>

Snacks Weekly on Data Science

Real-time Spatial and Temporal Forecasting [Lyft]

Real-time Spatial and Temporal Forecasting [Lyft]

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