A time series is a sequence of observations that are arranged according to the time of their outcome. The reasons of doing time series analysis are diverse, depending on the background of applications. Statisticians usually view a time series as a realization from a stochastic process. A fundamental task is to unveil the probability law that governs the observed time series. For instance, we wish to gain a better understanding of the data generating mechanism, the prediction of future values. 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. In this course, our flow is to spend around two thirds of time in the treatment from a traditional look of time series then to some recent models such as GARCH, long memory models and nonparametric methods. The course level is set for master students.

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

主要: Time Series: Applications to Finance with R and S-Plus
作者： Chan
新功能介紹
出版社：華泰文化
新功能介紹
出版日期：2011/04/06
語言：英文


參考: Time Series Analysis: With Applications in R (Springer Texts in Statistics) 2nd 版本
作者 Jonathan D. Cryer (Author), Kung-Sik Chan (Author)