Agentic Time Series Forecasting

ODSC AI West 2026

Oct 27, 2026 · Time to be announced

Large time models, or foundation forecasting models, have transformed how forecasting is done. We now have access to large pretrained models capable of zero-shot inference, allowing us to use these models at scale. However, there are still many ways we can improve these models with capabilities that are not native to them. In this talk, we first present how synthetic data can help improve the accuracy of a model. Synthetic data is cheap to create and can overcome the issues of data scarcity or the difficulty of getting more data. Then, we discuss conformal predictions to provide a better quantification of uncertainty. Conformal predictions are a distribution-free and model-agnostic approach to have more representative prediction intervals. We also explore reconciliation techniques for better accuracy. This is especially important in situations where one level must aggregate to another, e.g.; cities to country. Finally, we apply all those concepts in a forecasting agent for a low-code approach.

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Hybrid · Hyatt Regency San Francisco Airport, Burlingame, California, United States

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