协变量驱动的时间序列数据广泛存在于生产生活的各个领域,例如经济因素影响下的期货交易成交量、每月失业人数等。对该类数据统计分析也成为未来研究的一个重要领域。由于其结构和相依关系的复杂性，到目前为止对该类型数据的统计分析仍处于起步阶段。为了能准确地描述这类数据，本项目从该类数据的自身特点出发，提出三类时间序列模型，从不同的角度构造模型的系数结构，同时考虑了未知参数和未知函数的估计问题。我们也关心系数是否受到协变量影响的检验问题。这些工作对时间序列的研究有着重要的意义。此外，我们将建立的模型应用于保险精算领域，给出新的精算模型，为风险预警和费率厘定提供更为合理准确的方法，该研究结果将对精算行业的发展起到重要的促进作用。

Covariate-driven time series data widely exists in various fields of production and life, for example, the futures trading volume under the influence of economic factors and monthly unemployment, etc. The statistical analysis of this kind of data has become an important area for future research. Because of the complexity of its structure and dependent relationship, so far, statistical analysis for this kind of data is still in the initial stage. In order to accurately describe this kind of data, we propose three kinds of time series models based on the characteristics of the data, which describe the coefficient structures of the models from different viewpoints. For these models, we consider the estimate problems of the unknown parameters and the unknown functions, and the test problems whether the coefficients are affected by the covariates. This work is of great significance to the study of time series. Furthermore, we will apply these models to actuarial field, and propose some new actuarial models. These results will provide a more reasonable methods for risk early-warning and rate making, and play an important role in promoting the actuarial development.