Autoregressive Modeling
Overview
Autoregressive models represent a sequence as successive predictions conditioned on earlier elements, enabling both forecasting and generation. Common approaches include Transformer and state-space models, order-agnostic or masked prediction, coarse-to-fine generation, and continuous-latent models. They are used for tasks ranging from language and speech to weather, motion, and physical simulation, with evaluations including long-horizon rollouts and benchmarks such as ERA5, M5, and LibriSpeech. A central challenge is balancing accurate, efficient stepwise prediction with error accumulation, stability, and reliable behavior over long horizons or under distribution shift.
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