Temporal textures are textures with motion. Examples include wavy water, rising steam and a crowd milling about. We model image sequences of temporal textures using the spatio-temporal autoregressive model (STAR). This model expresses each pixel as a linear combination of surrounding pixels lagged both in space and in time. The model provides a basis both for recognition and synthesis. We show how the least squares method can accurately estimate model parameters for large, causal neighborhoods with more than 1000 parameters. Synthesis results show that the model can adequately capture the spatial and temporal characteristics of many temporal textures.