TimeDiT combines diffusion transformers with time series modeling to create a versatile foundation model that addresses unique challenges in temporal data.

• Tackles domain-specific challenges like missing values and multi-resolution characteristics
• Improves upon deterministic autoregressive transformers by accounting for inherent uncertainties
• Enables integration of physical constraints into the modeling process
• Applicable across engineering and medical domains where time series data is prevalent

This research bridges a critical gap between foundation models (successful in text/video) and the complex world of time series data, offering a powerful new approach for predictions and analysis in fields requiring temporal modeling.

TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Model