98年第2學期-4849 時間序列 課程資訊
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本課程名額為 70人，已有8 人選讀，尚餘名額62人。
The course objectives will be aimed to learn how to model time series data and analyze time series for relevant arising problems. We will not get into too many thoretical proofs of theorems but will have overviews on the properties required for methods. About 25% of computation loads will be expeceted. We will follow the subjects of the textbook but the contents for the classes will be delivered by integrating with other references as listed.
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.