This seminar explored foundation models for time series forecasting, focusing on the use of large language models as global, cross-domain forecasters. I discussed the shift from local, dataset-specific models to large pre-trained approaches, along with key challenges such as time-series tokenization, scaling behavior, and probabilistic zero-shot forecasting.
The presentation included a hands-on tutorial using Chronos and a comparison between LLM-based forecasters and global deep learning time series models, highlighting where these methods succeed and where they still fall short.
Source: CIV-ML’s Seminars