Webinar
Foundation Models for Time-Series
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Time-series data permeates our world, pulsing through financial markets, meteorological patterns, sensor networks, and countless other domains. Despite their ubiquity, generative AI techniques for time-series remain far less explored compared to the well-established approaches in language, image, and audio processing.
This presentation delves into the cutting-edge landscape of foundation models for time-series analysis. We will explore specialized neural architectures designed to unravel the complex temporal dynamics of sequential data and demonstrate how open-source models can unlock powerful predictive capabilities and insights.
Further, we demonstrate semantic search over time-series using the open-source vector database Milvus and how to build RAG and agentic systems with this.
Meet the Speaker
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Stefan Webb
Developer Advocate, Zilliz
Stefan Webb is a Developer Advocate at Zilliz, where he advocates for the open-source vector database, Milvus. Prior to this, he spent three years in industry as an Applied ML Researcher at Twitter and Meta, collaborating with product teams to tackle their most complex challenges. Stefan holds a PhD from the University of Oxford and has published papers at prestigious machine learning conferences such as NeurIPS, ICLR, and ICML. He is passionate about generative AI and is eager to leverage his deep technical expertise to contribute to the open-source community.