À propos de ce cours
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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Cours en ligne à 100 %

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Calendar

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.
Intermediate Level

Niveau intermédiaire

Clock

Recommandé : 9 hours/week

Approx. 22 heures pour terminer
Comment Dots

English

Sous-titres : English

Compétences que vous acquerrez

Time SeriesTime Series ForecastingTime Series AnalysisTime Series Models
Globe

Cours en ligne à 100 %

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Calendar

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.
Intermediate Level

Niveau intermédiaire

Clock

Recommandé : 9 hours/week

Approx. 22 heures pour terminer
Comment Dots

English

Sous-titres : English

Programme du cours : ce que vous apprendrez dans ce cours

1

Section
Clock
3 heures pour terminer

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 vidéos (Total 79 min), 4 lectures, 2 quiz
Video12 vidéos
Week 1 Welcome Video3 min
Getting Started in R: Download and Install R on Windows5 min
Getting Started in R: Download and Install R on Mac2 min
Getting Started in R: Using Packages7 min
Concatenation, Five-number summary, Standard Deviation5 min
Histogram in R6 min
Scatterplot in R3 min
Review of Basic Statistics I - Simple Linear Regression6 min
Reviewing Basic Statistics II More Linear Regression8 min
Reviewing Basic Statistics III - Inference12 min
Reviewing Basic Statistics IV9 min
Reading4 lectures
Welcome to Week 11 min
Getting Started with R10 min
Basic Statistics Review (with linear regression and hypothesis testing)10 min
Measuring Linear Association with the Correlation Function10 min
Quiz2 exercices pour s'entraîner
Visualization4 min
Basic Statistics Review18 min

2

Section
Clock
2 heures pour terminer

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 vidéos (Total 54 min), 1 lecture, 3 quiz
Video10 vidéos
Introduction1 min
Time plots8 min
First Intuitions on (Weak) Stationarity2 min
Autocovariance function9 min
Autocovariance coefficients6 min
Autocorrelation Function (ACF)5 min
Random Walk9 min
Introduction to Moving Average Processes3 min
Simulating MA(2) process6 min
Reading1 lecture
All slides together for the next two lessons10 min
Quiz3 exercices pour s'entraîner
Noise Versus Signal4 min
Random Walk vs Purely Random Process2 min
Time plots, Stationarity, ACV, ACF, Random Walk and MA processes20 min

3

Section
Clock
4 heures pour terminer

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 vidéos (Total 112 min), 7 lectures, 4 quiz
Video13 vidéos
Stationarity - Intuition and Definition13 min
Stationarity - First Examples...White Noise and Random Walks9 min
Stationarity - First Examples...ACF of Moving Average10 min
Series and Series Representation8 min
Backward shift operator5 min
Introduction to Invertibility12 min
Duality9 min
Mean Square Convergence (Optional)7 min
Autoregressive Processes - Definition, Simulation, and First Examples9 min
Autoregressive Processes - Backshift Operator and the ACF10 min
Difference equations7 min
Yule - Walker equations6 min
Reading7 lectures
Stationarity - Examples -White Noise, Random Walks, and Moving Averages10 min
Stationarity - Intuition and Definition10 min
Stationarity - ACF of a Moving Average10 min
All slides together for lesson 2 and 410 min
Autoregressive Processes- Definition and First Examples10 min
Autoregressive Processes - Backshift Operator and the ACF10 min
Yule - Walker equations - Slides10 min
Quiz4 exercices pour s'entraîner
Stationarity14 min
Series, Backward Shift Operator, Invertibility and Duality30 min
AR(p) and the ACF4 min
Difference equations and Yule-Walker equations30 min

4

Section
Clock
4 heures pour terminer

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 vidéos (Total 69 min), 3 lectures, 3 quiz
Video8 vidéos
Partial Autocorrelation and the PACF First Examples10 min
Partial Autocorrelation and the PACF - Concept Development8 min
Yule-Walker Equations in Matrix Form8 min
Yule Walker Estimation - AR(2) Simulation17 min
Yule Walker Estimation - AR(3) Simulation5 min
Recruitment data - model fitting8 min
Johnson & Johnson-model fitting8 min
Reading3 lectures
Partial Autocorrelation and the PACF First Examples10 min
Partial Autocorrelation and the PACF: Concept Development10 min
All slides together for the next two lessons10 min
Quiz3 exercices pour s'entraîner
Partial Autocorrelation4 min
Yule-Walker in matrix form and Yule-Walker estimation20 min
'LakeHuron' dataset40 min
4.6
Briefcase

83%

a bénéficié d'un avantage concret dans sa carrière grâce à ce cours

Meilleurs avis

par 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.

par 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.

Enseignants

Tural Sadigov

Lecturer
Applied Mathematics

William Thistleton

Associate Professor
Applied Mathematics

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Foire Aux Questions

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