About this Course
4.6
212 ratings
54 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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Intermediate Level

Intermediate Level

Clock

Suggested: 9 hours/week

Approx. 22 hours to complete
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English

Subtitles: English

Skills you will gain

Time SeriesTime Series ForecastingTime Series AnalysisTime Series Models
Globe

100% online courses

Start instantly and learn at your own schedule.
Calendar

Flexible deadlines

Reset deadlines in accordance to your schedule.
Intermediate Level

Intermediate Level

Clock

Suggested: 9 hours/week

Approx. 22 hours to complete
Comment Dots

English

Subtitles: English

Syllabus - What you will learn from this course

1

Section
Clock
3 hours to complete

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 videos (Total 79 min), 4 readings, 2 quizzes
Video12 videos
Week 1 Welcome Video3m
Getting Started in R: Download and Install R on Windows5m
Getting Started in R: Download and Install R on Mac2m
Getting Started in R: Using Packages7m
Concatenation, Five-number summary, Standard Deviation5m
Histogram in R6m
Scatterplot in R3m
Review of Basic Statistics I - Simple Linear Regression6m
Reviewing Basic Statistics II More Linear Regression8m
Reviewing Basic Statistics III - Inference12m
Reviewing Basic Statistics IV9m
Reading4 readings
Welcome to Week 11m
Getting Started with R10m
Basic Statistics Review (with linear regression and hypothesis testing)10m
Measuring Linear Association with the Correlation Function10m
Quiz2 practice exercises
Visualization4m
Basic Statistics Review18m

2

Section
Clock
2 hours to complete

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 videos (Total 54 min), 1 reading, 3 quizzes
Video10 videos
Introduction1m
Time plots8m
First Intuitions on (Weak) Stationarity2m
Autocovariance function9m
Autocovariance coefficients6m
Autocorrelation Function (ACF)5m
Random Walk9m
Introduction to Moving Average Processes3m
Simulating MA(2) process6m
Reading1 reading
All slides together for the next two lessons10m
Quiz3 practice exercises
Noise Versus Signal4m
Random Walk vs Purely Random Process2m
Time plots, Stationarity, ACV, ACF, Random Walk and MA processes20m

3

Section
Clock
4 hours to complete

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 videos (Total 112 min), 7 readings, 4 quizzes
Video13 videos
Stationarity - Intuition and Definition13m
Stationarity - First Examples...White Noise and Random Walks9m
Stationarity - First Examples...ACF of Moving Average10m
Series and Series Representation8m
Backward shift operator5m
Introduction to Invertibility12m
Duality9m
Mean Square Convergence (Optional)7m
Autoregressive Processes - Definition, Simulation, and First Examples9m
Autoregressive Processes - Backshift Operator and the ACF10m
Difference equations7m
Yule - Walker equations6m
Reading7 readings
Stationarity - Examples -White Noise, Random Walks, and Moving Averages10m
Stationarity - Intuition and Definition10m
Stationarity - ACF of a Moving Average10m
All slides together for lesson 2 and 410m
Autoregressive Processes- Definition and First Examples10m
Autoregressive Processes - Backshift Operator and the ACF10m
Yule - Walker equations - Slides10m
Quiz4 practice exercises
Stationarity14m
Series, Backward Shift Operator, Invertibility and Duality30m
AR(p) and the ACF4m
Difference equations and Yule-Walker equations30m

4

Section
Clock
4 hours to complete

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 videos (Total 69 min), 3 readings, 3 quizzes
Video8 videos
Partial Autocorrelation and the PACF First Examples10m
Partial Autocorrelation and the PACF - Concept Development8m
Yule-Walker Equations in Matrix Form8m
Yule Walker Estimation - AR(2) Simulation17m
Yule Walker Estimation - AR(3) Simulation5m
Recruitment data - model fitting8m
Johnson & Johnson-model fitting8m
Reading3 readings
Partial Autocorrelation and the PACF First Examples10m
Partial Autocorrelation and the PACF: Concept Development10m
All slides together for the next two lessons10m
Quiz3 practice exercises
Partial Autocorrelation4m
Yule-Walker in matrix form and Yule-Walker estimation20m
'LakeHuron' dataset40m
4.6
Briefcase

83%

got a tangible career benefit from this course

Top Reviews

By 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.

By 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.

Instructors

Tural Sadigov

Lecturer
Applied Mathematics

William Thistleton

Associate Professor
Applied Mathematics

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Frequently Asked Questions

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