This paper argues that time series modeling can be advanced by incorporating dynamical systems theory, which can provide better long-term forecasting, theoretical insight into mechanisms underlying time series generation, and more. This can be achieved through dynamical systems reconstruction, a class of ML/AI approaches that infer surrogate models of the underlying systems from data.