Acerca de este Curso
4.6
431 calificaciones
112 revisiones

100 % en línea

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Fechas límite flexibles

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Nivel intermedio

Aprox. 25 horas para completar

Sugerido: 9 hours/week...

Inglés (English)

Subtítulos: Inglés (English)

Habilidades que obtendrás

Time Series ForecastingTime SeriesTime Series Models

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel intermedio

Aprox. 25 horas para completar

Sugerido: 9 hours/week...

Inglés (English)

Subtítulos: Inglés (English)

Programa - Qué aprenderás en este curso

Semana
1
3 horas para completar

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....
12 videos (Total 79 minutos), 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 lecturas
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 ejercicios de práctica
Visualization4m
Basic Statistics Review18m
Semana
2
2 horas para completar

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....
10 videos (Total 54 minutos), 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 lectura
All slides together for the next two lessons10m
3 ejercicios de práctica
Noise Versus Signal4m
Random Walk vs Purely Random Process2m
Time plots, Stationarity, ACV, ACF, Random Walk and MA processes20m
Semana
3
4 horas para completar

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. ...
13 videos (Total 112 minutos), 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 lecturas
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 ejercicios de práctica
Stationarity14m
Series, Backward Shift Operator, Invertibility and Duality30m
AR(p) and the ACF4m
Difference equations and Yule-Walker equations30m
Semana
4
4 horas para completar

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. ...
8 videos (Total 69 minutos), 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 lecturas
Partial Autocorrelation and the PACF First Examples10m
Partial Autocorrelation and the PACF: Concept Development10m
All slides together for the next two lessons10m
3 ejercicios de práctica
Partial Autocorrelation4m
Yule-Walker in matrix form and Yule-Walker estimation20m
'LakeHuron' dataset40m
4.6
112 revisionesChevron Right

39%

comenzó una nueva carrera después de completar estos cursos

26%

consiguió un beneficio tangible en su carrera profesional gracias a este curso

Principales revisiones

por 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.

por 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.

Instructores

Avatar

Tural Sadigov

Lecturer
Applied Mathematics
Avatar

William Thistleton

Associate Professor
Applied Mathematics

Acerca de Universidad Estatal de Nueva York

The State University of New York, with 64 unique institutions, is the largest comprehensive system of higher education in the United States. Educating nearly 468,000 students in more than 7,500 degree and certificate programs both on campus and online, SUNY has nearly 3 million alumni around the globe....

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