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Earn a Certificate upon completion

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#### English

Subtitles: English

### Skills you will gain

Time Series ForecastingTime SeriesTime Series Models

## 36%

started a new career after completing these courses

## 26%

got a tangible career benefit from this course

#### Shareable Certificate

Earn a Certificate upon completion

#### 100% online

Start instantly and learn at your own schedule.

#### English

Subtitles: English

## Syllabus - What you will learn from this course

Content Rating93%(5,280 ratings)
Week
1

## Week 1

3 hours to complete

## WEEK 1: Basic Statistics

3 hours to complete
12 videos (Total 79 min), 4 readings, 2 quizzes
12 videos
Week 1 Welcome Video3m
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
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

## Week 2

2 hours to complete

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

2 hours to complete
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
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

## Week 3

4 hours to complete

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

4 hours to complete
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
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

## Week 4

4 hours to complete

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

4 hours to complete
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
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