This work investigates the potential of employing the approach Compressed Sensing Dynamic Mode Decomposition (CS-DMD) in the context of time-varying wireless channels. To the best of the authors’ knowledge, this marks the first instance of utilizing CS-DMD for pilot-based channel estima- tion in Orthogonal Frequency Division Multiplexing (OFDM) systems. The effectiveness of this method is compared with two advanced deep learning-based channel estimation techniques: Interpolation-ResNet and Learned Approximate Message Passing (LAMP). Furthermore, we leverage the advantageous character- istics of DMD in analyzing complex nonlinear dynamic systems to predict the future state of the channel, thereby reducing the required pilot signals. Simulation results show that utilizing CS- DMD can achieve superior channel estimation performance with less pilot overhead.