ARTICLES 

Big data has become a new instrument for international political forecasting. The success in big data forecasting is premised upon the minimally acceptable stability and continuity of an event. Big data forecasting is made up of three stages, namely data preparation, model building and model application. Data preparation is composed mainly of data access and pretreatment, at which stage the researchers are confronted with both normative and substantive constraints. At the stage of model building, the researchers construct the algorithms and models, which would significantly influence the effectiveness of forecasting. At the stage of model application and feedback, the researchers would first make predictions on the basis of their models, and then test, evaluate and adjust the modes in terms of the results. Among other things, the contextual information of an event and the path of an object play a substantial role in accurate prediction. Empirically speaking, the more the above constraints have been overcome, the more accurate a forecasting would be. Additionally, big data forecasting is evaluated predominantly by cross-validation, which does not require a deep exploration into the connections between variables. Moreover, causality is not always indispensable for the prediction of some events. Nonetheless, causality is merely the foundation of model building, but would exert profound effects upon big data forecasting during the whole process.