Traffic prediction acts as a fundamental function in the management and optimization of networks and services. There are emerging requirements for extraordinary traffic prediction, including variable-term traffic series and comprehensive traffic behavior. Compared to ordinary traffic prediction, these demands call for solutions to mine rich information and predict business load under broader conditions. In this work, we propose X-Trafformer, a graph spatiotemporal transformer model, for extraordinary traffic prediction. Unlike conventional techniques that model traffic sequences, we transform traffic sequences into traffic behaviors under generalized objects, where behaviors are initiated by generalized objects and possess specific behavioral attributes. It allows for the prediction of multiple variables in traffic data across different networks and services, leveraging the matching of behavioral attributes among behavior objects and events. X-Trafformer incorporates multiple interrelated graph structures to capture fine-grained attribute spatiotemporal associations and coarse-grained object spatiotemporal distributions, forming the foundation for accurate prediction. Evaluation on real and representative traffic scenarios (communication traffic from Italian Telecom and business traffic from Tmall) demonstrates X-Trafformer's exceptional prediction performance at low computational costs.