課程目標及內涵 (Course Objectives and Contents)
就課程概述所規畫之課程內容範疇，以大學部學生為授課對象，因此課程目標上將比課程概述所規畫之課程內容範疇較為應用及方法上的理解及對資料方面的分析與建模。理論的部份以大學部學生所受之基礎訓練下，僅限於關鍵方法與模型上主要推導為主。

課程目標:

建立對時間序列模型及方法的觀念與應用
能對時間序列資料進行一般的分析
使用R語言分析時間序列


課程內容包含以下:

Time series data
Stationary time series
ARMAmodels
Estimation of ARMA models
Forecasting

Course Objectives and Contents
The course content scope of the course overview of the course is based on the university department as the subject of the course. Therefore, the course objective is to compare the course content scope of the course overview of the course to understand the course and analyze and model the data. The theoretical part is based on the basic training received by university students, and is limited to the main promotion of key methods and models.

Course Target:

Establishing concepts and applications for time sequence models and methods
Able to conduct general analysis of time sequence data
Analyze time sequences using R language


The course content includes the following:

Time series data
Stationary time series
ARMAmodels
Estimation of ARMA models
Forecasting

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.

Time Series Analysis
Jonathan D. Cryer • Kung-Sik Chan
With Applications in R

Time Series Analysis
Jonathan D. Cryer • Kung-Sik Chan
With Applications in R