本課程旨在引導學生深入了解時間序列資料的特性，並掌握處理與分析時間序列資料的核心統計方法。透過結合理論學習、軟體應用及實務研究，課程目標包括：
1. 建立時間序列基礎概念： 使學生理解時間序列資料（如季節性、趨勢、循環等）的獨特性與分析的重要性。
2. 掌握多元分析技術： 訓練學生熟練運用迴歸分析、分解法、指數平滑法及 Box-Jenkins (ARIMA) 方法等經典模型進行建模與分析。
3. 強化實務應用能力： 培養學生運用專業統計軟體（主要以R語言）處理真實世界的資料，進行有效的預測與評估模型表現。

This course aims to guide students to gain an in-depth understanding of the characteristics of time series data and master the core statistical methods for processing and analyzing time series data. By combining theoretical learning, software application and practical research, course objectives include:
1. Establish basic concepts of time series: enable students to understand the uniqueness and importance of analysis of time series data (such as seasonality, trends, cycles, etc.).
2. Master multivariate analysis techniques: Students are trained to skillfully use classic models such as regression analysis, decomposition method, exponential smoothing method, and Box-Jenkins (ARIMA) method for modeling and analysis.
3. Strengthen practical application abilities: Cultivate students to use professional statistical software (mainly in R language) to process real-world data and make effective predictions and evaluate model performance.

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.

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. 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.

Bowerman, O’Connell, and Koehler (2005) Forecasting, Time Series, and Regression, 4th edition

Hyndman, R.~J. & Athanasopoulos, G. (2021) Forecasting: principles and practice, 3rd edition, OTexts.org/fpp3/

Bowerman, O’Connell, and Koehler (2005) Forecasting, Time Series, and Regression, 4th edition

Hyndman, R.~J. & Athanasopoulos, G. (2021) Forecasting: principles and practice, 3rd edition, OTexts.org/fpp3/