Optimal control strategies for wave energy converters (WECs) commonly require noncausal knowledge of the incident wave to maximize energy production. To enable these control methods, the prediction capabilities of two autoregressive (AR) models are evaluated. This work utilized autoregressive methods to predict the wave excitation force since they can be implemented in real time to adapt to changing conditions. The two models evaluated in this work are AR and AR with exogenous inputs (ARX) models. These models describe the wave propagation between two devices. To substantiate the validity of the predictions presented here, the wave excitation force was also estimated using an extended Kalman filter (EKF). The EKF incorporated nonlinear heave models of each body to determine the wave excitation force that was formulated as a harmonic disturbance to each system. The combination of the EKF and the AR models presents an opportunity to evaluate the prediction capabilities of what can be currently implemented on board WECs in real time; there is no need for offline training or postprocessed filtering of the wave. The ARX model incorporating excitation force data from other deployed bodies (the exogenous input) is shown to significantly improve the performance of the wave excitation force prediction. It is concluded that WECs in a wave farm may be able to improve their energy harvesting performance through enhancing their prediction capabilities by using wave estimation data gathered from other WECs. Experimental data from a two-body wave tank test is used for this work.