This work proposes a windowed least-squares approach for model reduction of dynamical systems. The method constructs reduced-order models by minimizing the residual over a sliding time window, which enables the method to adapt to the local dynamics and improve accuracy compared to traditional least-squares approaches that consider the entire time domain globally. The windowed formulation provides a natural framework for handling time-dependent phenomena and enables parallelization across time windows. Numerical experiments demonstrate the method's effectiveness for both linear and nonlinear dynamical systems.