课程信息
4.6
250 ratings
66 reviews
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!...
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100% 在线课程

立即开始,按照自己的计划学习。
Calendar

可灵活调整截止日期

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Intermediate Level

中级

Clock

建议:8 hours/week

完成时间大约为22 小时
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English

字幕:English

您将获得的技能

Time Series ForecastingTime SeriesTime Series Models
Globe

100% 在线课程

立即开始,按照自己的计划学习。
Calendar

可灵活调整截止日期

根据您的日程表重置截止日期。
Intermediate Level

中级

Clock

建议:8 hours/week

完成时间大约为22 小时
Comment Dots

English

字幕:English

教学大纲 - 您将从这门课程中学到什么

1

章节
Clock
完成时间为 3 小时

WEEK 1: Basic Statistics

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....
Reading
12 个视频(共 79 分钟), 4 个阅读材料, 2 个测验
Video12 个视频
Week 1 Welcome Video3分钟
Getting Started in R: Download and Install R on Windows5分钟
Getting Started in R: Download and Install R on Mac2分钟
Getting Started in R: Using Packages7分钟
Concatenation, Five-number summary, Standard Deviation5分钟
Histogram in R6分钟
Scatterplot in R3分钟
Review of Basic Statistics I - Simple Linear Regression6分钟
Reviewing Basic Statistics II More Linear Regression8分钟
Reviewing Basic Statistics III - Inference12分钟
Reviewing Basic Statistics IV9分钟
Reading4 个阅读材料
Welcome to Week 11分钟
Getting Started with R10分钟
Basic Statistics Review (with linear regression and hypothesis testing)10分钟
Measuring Linear Association with the Correlation Function10分钟
Quiz2 个练习
Visualization4分钟
Basic Statistics Review18分钟

2

章节
Clock
完成时间为 2 小时

Week 2: Visualizing Time Series, and Beginning to Model Time Series

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....
Reading
10 个视频(共 54 分钟), 1 个阅读材料, 3 个测验
Video10 个视频
Introduction1分钟
Time plots8分钟
First Intuitions on (Weak) Stationarity2分钟
Autocovariance function9分钟
Autocovariance coefficients6分钟
Autocorrelation Function (ACF)5分钟
Random Walk9分钟
Introduction to Moving Average Processes3分钟
Simulating MA(2) process6分钟
Reading1 个阅读材料
All slides together for the next two lessons10分钟
Quiz3 个练习
Noise Versus Signal4分钟
Random Walk vs Purely Random Process2分钟
Time plots, Stationarity, ACV, ACF, Random Walk and MA processes20分钟

3

章节
Clock
完成时间为 4 小时

Week 3: Stationarity, MA(q) and AR(p) processes

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. ...
Reading
13 个视频(共 112 分钟), 7 个阅读材料, 4 个测验
Video13 个视频
Stationarity - Intuition and Definition13分钟
Stationarity - First Examples...White Noise and Random Walks9分钟
Stationarity - First Examples...ACF of Moving Average10分钟
Series and Series Representation8分钟
Backward shift operator5分钟
Introduction to Invertibility12分钟
Duality9分钟
Mean Square Convergence (Optional)7分钟
Autoregressive Processes - Definition, Simulation, and First Examples9分钟
Autoregressive Processes - Backshift Operator and the ACF10分钟
Difference equations7分钟
Yule - Walker equations6分钟
Reading7 个阅读材料
Stationarity - Examples -White Noise, Random Walks, and Moving Averages10分钟
Stationarity - Intuition and Definition10分钟
Stationarity - ACF of a Moving Average10分钟
All slides together for lesson 2 and 410分钟
Autoregressive Processes- Definition and First Examples10分钟
Autoregressive Processes - Backshift Operator and the ACF10分钟
Yule - Walker equations - Slides10分钟
Quiz4 个练习
Stationarity14分钟
Series, Backward Shift Operator, Invertibility and Duality30分钟
AR(p) and the ACF4分钟
Difference equations and Yule-Walker equations30分钟

4

章节
Clock
完成时间为 4 小时

Week 4: AR(p) processes, Yule-Walker equations, PACF

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. ...
Reading
8 个视频(共 69 分钟), 3 个阅读材料, 3 个测验
Video8 个视频
Partial Autocorrelation and the PACF First Examples10分钟
Partial Autocorrelation and the PACF - Concept Development8分钟
Yule-Walker Equations in Matrix Form8分钟
Yule Walker Estimation - AR(2) Simulation17分钟
Yule Walker Estimation - AR(3) Simulation5分钟
Recruitment data - model fitting8分钟
Johnson & Johnson-model fitting8分钟
Reading3 个阅读材料
Partial Autocorrelation and the PACF First Examples10分钟
Partial Autocorrelation and the PACF: Concept Development10分钟
All slides together for the next two lessons10分钟
Quiz3 个练习
Partial Autocorrelation4分钟
Yule-Walker in matrix form and Yule-Walker estimation20分钟
'LakeHuron' dataset40分钟
4.6
Briefcase

83%

通过此课程获得实实在在的工作福利

热门审阅

创建者 MSFeb 28th 2018

I have not completed the course yet, working on week 5. If you have some Math background, this course gives a good practical introduction to Time Series Analysis. I recommend it.

创建者 RSMar 18th 2018

Really great lectures and clearly explaining the concepts and complicated models. In my opinion, a bit of practical applications of these models on Panel Data should be included.

讲师

Tural Sadigov

Lecturer
Applied Mathematics

William Thistleton

Associate Professor
Applied Mathematics

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