meteorology,astronomy,geology,agriculture and forestry,biology,medical science,chemical industry,metallurgy,machinery,economics,management et cetera in our country.Especially,in economics,more and more practical workers begin know about and use time series analysis methods.With the deepening of innovation and the developing of economics,a great deal of data must be deal with and need more scientific methods to forecast and decide in economics field.So,popularizing time series analysis methods is needed. This course mainly includes the base notations of the time series analysis, the base procedures of building time series models,memory function, autoregressive models(AR),moving average models(MA),autoregressive moving average models(ARMA),the autocorrelation function and partial autocorrelation function of the stationary models,the identifying methods of models,models with trend,unit root test,deterministic trend and stochastic trend,removing the trend,autoregressive integrated moving average models(ARIMA),parameter estimation,diagnostic checking, forecasting,modeling seasonal data,transfer function model,intervention analysis,Outlier Detection,regressive models with ARIMA error, autoregressive conditional heteroscedastic models(ARCH),generalized ARCH models(GARCH),vector autoregressive models(VAR),Granger causality test, structural VAR,variance decomposition,cointegration and error-correction models. 三、课程性质与教学目的 时间序列分析是经济类统计学专业和金融类专业的专业必修课。 开设本课程的目的在于使学生在原专业基础课概率论和统计学课程的基础上，理 解时间序列的基本概念，熟悉时间序列的基本理论，掌握时间序列各种建模的方法与 3 3 meteorology, astronomy, geology, agriculture and forestry, biology, medical science, chemical industry, metallurgy, machinery, economics, management et cetera in our country. Especially, in economics, more and more practical workers begin know about and use time series analysis methods. With the deepening of innovation and the developing of economics, a great deal of data must be deal with and need more scientific methods to forecast and decide in economics field. So, popularizing time series analysis methods is needed. This course mainly includes the base notations of the time series analysis, the base procedures of building time series models, memory function, autoregressive models(AR), moving average models(MA), autoregressive moving average models(ARMA), the autocorrelation function and partial autocorrelation function of the stationary models, the identifying methods of models, models with trend， unit root test，deterministic trend and stochastic trend, removing the trend, autoregressive integrated moving average models(ARIMA), parameter estimation, diagnostic checking, forecasting, modeling seasonal data, transfer function model, intervention analysis, Outlier Detection, regressive models with ARIMA error, autoregressive conditional heteroscedastic models(ARCH), generalized ARCH models(GARCH), vector autoregressive models(VAR), Granger causality test, structural VAR, variance decomposition, cointegration and error-correction models. 三、课程性质与教学目的 时间序列分析是经济类统计学专业和金融类专业的专业必修课。 开设本课程的目的在于使学生在原专业基础课概率论和统计学课程的基础上，理 解时间序列的基本概念，熟悉时间序列的基本理论，掌握时间序列各种建模的方法与