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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/practical-time-series-analysis
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课程名称：实用时间序列分析 课程概述：欢迎参加《实用时间序列分析》课程！许多人都是“偶然”成为数据分析师的，他们在科学、商业或工程领域受过训练，但在面对数据时缺乏正式的分析培训。本课程旨在为具备一定技术基础的学习者提供超越“食谱”方法的知识，同时帮助他们专注于深化专业主题理解的常规展示和分析。 在实用时间序列分析中，我们研究表示顺序信息的数据集，例如股票价格、年度降雨量、日斑活动、农产品价格等。我们讨论多种数学模型，描述生成这些数据类型的过程，同时研究图形表示，以提供对数据的深入理解。最后，我们学习如何进行预测，合理预期未来的趋势。 请花几分钟时间浏览课程网站，您将找到支持性书面材料的视频讲座以及帮助强调重要点的测验。课程使用R语言，这是一种免费的S语言实现，专业环境且相对易于学习。 您可以与其他学习者讨论课程内容，请花时间介绍自己！学习时间序列分析可能需要付出努力，我们努力以您能够理解的方式呈现“关键任务”的概念，同时确保您能够立即投入实践。希望您喜欢这门课程！ 课程大纲： 第1周：基本统计 描述：在第一周，我们将展示如何在Windows和Mac上下载和安装R，并回顾在本课程中需要的推断和描述统计的基础知识。 第2周：可视化时间序列与初步建模 描述：本周我们开始探索和可视化作为获取数据集的时间序列，并初步开发分析时间序列数据所需的数学模型。 第3周：平稳性，MA(q)和AR(p)过程 描述：在第3周，我们介绍了一些时间序列分析中的重要概念：平稳性、反向移位算子、可逆性和对偶性。开始研究自回归过程和Yule-Walker方程。 第4周：AR(p)过程，Yule-Walker方程，PACF 描述：本周介绍偏自相关。我们进一步研究Yule-Walker方程，并将目前学到的知识应用于一些真实的数据集。 第5周：赤池信息量准则(AIC)、混合模型、综合模型 描述：在第5周，我们开始使用赤池信息量准则评估模型，介绍混合模型如ARMA、ARIMA，并对一些真实数据集进行建模。 第6周：季节性，SARIMA，预测 描述：在课程的最后一周，我们引入了另一种模型：SARIMA。我们将SARIMA模型应用于不同数据集并开始进行预测。
Name:WEEK 1: Basic Statistics
Description:During this first week, we show how to download and install R on Windows and the Mac. We review those basics of inferential and descriptive statistics that you'll need during the course.
Name:Week 2: Visualizing Time Series, and Beginning to Model Time Series
Description:In this week, we begin to explore and visualize time series available as acquired data sets. We also take our first steps on developing the mathematical models needed to analyze time series data.
Name:Week 3: Stationarity, MA(q) and AR(p) processes
Description:In Week 3, we introduce few important notions in time series analysis: Stationarity, Backward shift operator, Invertibility, and Duality. We begin to explore Autoregressive processes and Yule-Walker equations.
Name:Week 4: AR(p) processes, Yule-Walker equations, PACF
Description:In this week, partial autocorrelation is introduced. We work more on Yule-Walker equations, and apply what we have learned so far to few real-world datasets.
Name:Week 5: Akaike Information Criterion (AIC), Mixed Models, Integrated Models
Description:In Week 5, we start working with Akaike Information criterion as a tool to judge our models, introduce mixed models such as ARMA, ARIMA and model few real-world datasets.
Name:Week 6: Seasonality, SARIMA, Forecasting
Description:In the last week of our course, another model is introduced: SARIMA. We fit SARIMA models to various datasets and start forecasting.
Welcome to Practical Time Series Analysis! Many of us are "accidental" data analysts. We trained in the sciences, business, or engineering and then found ourselves confronted with data for which we have no formal analytic training. This course is designed for people with some technical competencies who would like more than a "cookbook" approach, but who still need to concentrate on the routine sorts of presentation and analysis that deepen the understanding of our professional topics. In practical Time Series Analysis we look at data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, the price of agricultural products, and more. We look at several mathematical models that might be used to describe the processes which generate these types of data. We also look at graphical representations that provide insights into our data. Finally, we also learn how to make forecasts that say intelligent things about what we might expect in the future. Please take a few minutes to explore the course site. You will find video lectures with supporting written materials as well as quizzes to help emphasize important points. The language for the course is R, a free implementation of the S language. It is a professional environment and fairly easy to learn. You can discuss material from the course with your fellow learners. Please take a moment to introduce yourself! Time Series Analysis can take effort to learn- we have tried to present those ideas that are "mission critical" in a way where you understand enough of the math to fell satisfied while also being immediately productive. We hope you enjoy the class!
