Об этом курсе
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

Промежуточный уровень

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Предполагаемая нагрузка: 9 hours/week

Прибл. 22 ч. на завершение
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English

Субтитры: English

Приобретаемые навыки

Time SeriesTime Series ForecastingTime Series AnalysisTime Series Models
Globe

Только онлайн-курсы

Начните сейчас и учитесь по собственному графику.
Calendar

Гибкие сроки

Назначьте сроки сдачи в соответствии со своим графиком.
Intermediate Level

Промежуточный уровень

Clock

Предполагаемая нагрузка: 9 hours/week

Прибл. 22 ч. на завершение
Comment Dots

English

Субтитры: English

Программа курса: что вы изучите

1

Раздел
Clock
3 ч. на завершение

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 видео (всего 79 мин.), 4 материалов для самостоятельного изучения, 2 тестов
Video12 видео
Week 1 Welcome Video3мин
Getting Started in R: Download and Install R on Windows5мин
Getting Started in R: Download and Install R on Mac2мин
Getting Started in R: Using Packages7мин
Concatenation, Five-number summary, Standard Deviation5мин
Histogram in R6мин
Scatterplot in R3мин
Review of Basic Statistics I - Simple Linear Regression6мин
Reviewing Basic Statistics II More Linear Regression8мин
Reviewing Basic Statistics III - Inference12мин
Reviewing Basic Statistics IV9мин
Reading4 материала для самостоятельного изучения
Welcome to Week 11мин
Getting Started with R10мин
Basic Statistics Review (with linear regression and hypothesis testing)10мин
Measuring Linear Association with the Correlation Function10мин
Quiz2 практического упражнения
Visualization4мин
Basic Statistics Review18мин

2

Раздел
Clock
2 ч. на завершение

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 видео (всего 54 мин.), 1 материал для самостоятельного изучения, 3 тестов
Video10 видео
Introduction1мин
Time plots8мин
First Intuitions on (Weak) Stationarity2мин
Autocovariance function9мин
Autocovariance coefficients6мин
Autocorrelation Function (ACF)5мин
Random Walk9мин
Introduction to Moving Average Processes3мин
Simulating MA(2) process6мин
Reading1 материал для самостоятельного изучения
All slides together for the next two lessons10мин
Quiz3 практического упражнения
Noise Versus Signal4мин
Random Walk vs Purely Random Process2мин
Time plots, Stationarity, ACV, ACF, Random Walk and MA processes20мин

3

Раздел
Clock
4 ч. на завершение

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 видео (всего 112 мин.), 7 материалов для самостоятельного изучения, 4 тестов
Video13 видео
Stationarity - Intuition and Definition13мин
Stationarity - First Examples...White Noise and Random Walks9мин
Stationarity - First Examples...ACF of Moving Average10мин
Series and Series Representation8мин
Backward shift operator5мин
Introduction to Invertibility12мин
Duality9мин
Mean Square Convergence (Optional)7мин
Autoregressive Processes - Definition, Simulation, and First Examples9мин
Autoregressive Processes - Backshift Operator and the ACF10мин
Difference equations7мин
Yule - Walker equations6мин
Reading7 материала для самостоятельного изучения
Stationarity - Examples -White Noise, Random Walks, and Moving Averages10мин
Stationarity - Intuition and Definition10мин
Stationarity - ACF of a Moving Average10мин
All slides together for lesson 2 and 410мин
Autoregressive Processes- Definition and First Examples10мин
Autoregressive Processes - Backshift Operator and the ACF10мин
Yule - Walker equations - Slides10мин
Quiz4 практического упражнения
Stationarity14мин
Series, Backward Shift Operator, Invertibility and Duality30мин
AR(p) and the ACF4мин
Difference equations and Yule-Walker equations30мин

4

Раздел
Clock
4 ч. на завершение

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 видео (всего 69 мин.), 3 материалов для самостоятельного изучения, 3 тестов
Video8 видео
Partial Autocorrelation and the PACF First Examples10мин
Partial Autocorrelation and the PACF - Concept Development8мин
Yule-Walker Equations in Matrix Form8мин
Yule Walker Estimation - AR(2) Simulation17мин
Yule Walker Estimation - AR(3) Simulation5мин
Recruitment data - model fitting8мин
Johnson & Johnson-model fitting8мин
Reading3 материала для самостоятельного изучения
Partial Autocorrelation and the PACF First Examples10мин
Partial Autocorrelation and the PACF: Concept Development10мин
All slides together for the next two lessons10мин
Quiz3 практического упражнения
Partial Autocorrelation4мин
Yule-Walker in matrix form and Yule-Walker estimation20мин
'LakeHuron' dataset40мин
4.6
Briefcase

83%

получил значимые преимущества в карьере благодаря этому курсу

Лучшие рецензии

автор: 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.

автор: 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.

Преподавателя

Tural Sadigov

Lecturer
Applied Mathematics

William Thistleton

Associate Professor
Applied Mathematics

О The State University of New 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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