Foundation Models for Time Series

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.


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