About this Course
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Learner Career Outcomes

36%

started a new career after completing these courses

26%

got a tangible career benefit from this course

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 25 hours to complete

Suggested: 9 hours/week...

English

Subtitles: English

Skills you will gain

Time Series ForecastingTime SeriesTime Series Models

Learner Career Outcomes

36%

started a new career after completing these courses

26%

got a tangible career benefit from this course

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 25 hours to complete

Suggested: 9 hours/week...

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1
3 hours to complete

WEEK 1: Basic Statistics

12 videos (Total 79 min), 4 readings, 2 quizzes
12 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
4 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
2 practice exercises
Visualization4m
Basic Statistics Review18m
Week
2
2 hours to complete

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

10 videos (Total 54 min), 1 reading, 3 quizzes
10 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
1 reading
All slides together for the next two lessons10m
3 practice exercises
Noise Versus Signal4m
Random Walk vs Purely Random Process2m
Time plots, Stationarity, ACV, ACF, Random Walk and MA processes20m
Week
3
4 hours to complete

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

13 videos (Total 112 min), 7 readings, 4 quizzes
13 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
7 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
4 practice exercises
Stationarity14m
Series, Backward Shift Operator, Invertibility and Duality30m
AR(p) and the ACF4m
Difference equations and Yule-Walker equations30m
Week
4
4 hours to complete

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

8 videos (Total 69 min), 3 readings, 3 quizzes
8 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
3 readings
Partial Autocorrelation and the PACF First Examples10m
Partial Autocorrelation and the PACF: Concept Development10m
All slides together for the next two lessons10m
3 practice exercises
Partial Autocorrelation4m
Yule-Walker in matrix form and Yule-Walker estimation20m
'LakeHuron' dataset40m
4.6
184 ReviewsChevron Right

Top reviews from Practical Time Series Analysis

By JMMar 21st 2019

This was a very good and detailed course. I liked this course for two reasons mainly:\n\nIt started from the basics of timeseries analysis, covering theory and secondly it took me gradually to r.

By LYAug 3rd 2019

A nice course which is practical as the name said, it balanced the portion of theories and practices. I used to not familiar with this topic, but now I consider myself much more familiar.

Instructors

Avatar

Tural Sadigov

Lecturer
Applied Mathematics
Avatar

William Thistleton

Associate Professor
Applied Mathematics

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Frequently Asked Questions

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