Traffic state prediction plays an important role in intelligent transportation systems. Aiming at the problem that the existing traffic forecasting models focus on the medium and micro level, and their temporal and spatial dimensions are single, an integrated model for macroscopic traffic state forecasting was proposed. Based on the traffic index, the time series prediction model was adopted to obtain the temporal predictive results in the time dimension, and the support vector regression model was adopted to obtain the spatial predictive results in the spatial dimension. The results of two models were fused in an integrated model. Through experiments on the traffic index cloud maps, the results show that significant improvement in the prediction accuracy can be achieved by the proposed integrated model compared with the single temporal or spatial model.