Advanced Time Series Analysis: From Probabilistic to Foundational Models

Time series analysis studies data that change as a function of time, such as stock market prices, weather patterns, or household electricity consumption. This seminar covers advanced techniques for analyzing time series, starting with probabilistic methods and progressing to state-of-the-art deep learning approaches, including neural architectures and foundation models. We will also explore connections between time series and other modalities, such as text and images/videos, to offer a comprehensive view of the field. The aim is for students to critically assess existing methods, understand their strengths and limitations, and identify potential directions for future research.

The seminar begins with three introductory lectures to establish foundational concepts, followed by three main blocks on time series analysis: classical approaches, modern deep leanring architectures, and foundational models.

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https://cms.sic.saarland/tsa25/