A disturbance predictor was proposed earlier for a simplified model predictive control (SMPC) algorithm. In this paper, a closed-loop transfer function is derived for the proposed predictor, and its performance is analyzed. The analysis shows that the predictor offers an improvement in disturbance rejection. A new optimization scheme is proposed for online determination of a tuning parameter employed in the disturbance predictor. The disturbance predictor is applied to three example problems for nonstationary ARIMA disturbances that commonly occur in many industries. A comparison with the generalized analytical predictor shows that the proposed disturbance predictor provides an improved control performance. In addition, a relationship between the tuning parameter of the Dahlin and SMPC algorithms is presented. This relationship enables the selection of the tuning parameter of the SMPC algorithm by using a desired value of the closed-loop time constant.