This course is to provide introductory lessons in time series for the third and fourth year of undergraduates. The goal is to let undergraduate senior students learn basic statistical properties of time series such as stationary distribution, ACF, PACF, and to learn traditional Box and Jenkin's time series linear models then they can perform simple analysis on time series using these methods and skills. Further, the course will also contain the topics about multiple time series using time series regression. Some newly and advanced methods may also be introduced.

This course is to provide introduction lessons in time series for the third and fourth year of undergraduates. The goal is to let undergraduate senior students learn basic statistical properties of time series such as stationary distribution, ACF, PACF, and to learn traditional Box and Jenkin's time series linear models then they can perform simple analysis on time series using these methods and skills. Further, the course will also contain the topics about multiple time series using time series regression. Some newly and advanced methods may also be introduced.

A time series is a sequence of observations that are arranged according to the time of their outcome. Many recent applications of time series receive much of attention in financial areas. However, time series analysis has also exhibited its importance across many scientific areas for a long history and the desire of such analysis is still going on. This course is to provide introductory lessons in time series for the third and fourth year of undergraduates, who have major interests in statistics, and have finished some elementary courses, such as statistics, calculus, linear algebra and regression. The courses will be in three parts. The first part will be on the focus of regression, including a quick overall review of basics and an extension of regression modeling particularly required for time series data. The second part is aimed at the ARIMA models and seasonal models, which are traditional treatments established by Box and Jenkins. The third part is to introduce some new advances in time series or more real applications of time series models. Served as an undergraduate course, the topics will not be expected to go in great depth but to learn basic approach and to prepare background for further advanced studies.

A time series is a sequence of observations that are arranged according to the time of their outcome. Many recent applications of time series receive much of attention in financial areas. However, time series analysis has also exhibited its importance across many scientific areas for a long history and the desire of such analysis is still going on. This course is to provide introduction lessons in time series for the third and four year of undergraduates, who have major interests in statistics, and have finished some elementary courses, such as statistics, calculations, linear algebra and regression. The courses will be in three parts. The first part will be on the focus of regression, including a quick overall review of basics and an extension of regression modeling particularly required for time series data. The second part is aimed at the ARIMA models and seasonal models, which are traditional treatments established by Box and Jenkins. The third part is to introduce some new advances in time series or more real applications of time series models. Served as an undergraduate course, the topics will not be expected to go in great depth but to learn basic approach and to prepare background for further advanced studies.

Elements of Forecasting, 4th Edition (Francis X. Diebold) --- 滄海代理

Elements of Forecasting, 4th Edition (Francis X. Diebold) --- Agent of Francis X. Diebold