About this Course
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지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

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중급 단계

완료하는 데 약 25시간 필요

권장: 9 hours/week...

영어

자막: 영어

귀하가 습득할 기술

Time Series ForecastingTime SeriesTime Series Models

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

탄력적인 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

완료하는 데 약 25시간 필요

권장: 9 hours/week...

영어

자막: 영어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 3시간 필요

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 min), 4 readings, 2 quizzes
12개의 동영상
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
4개의 읽기 자료
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
2개 연습문제
Visualization4m
Basic Statistics Review18m
2
완료하는 데 2시간 필요

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 min), 1 reading, 3 quizzes
10개의 동영상
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
1개의 읽기 자료
All slides together for the next two lessons10m
3개 연습문제
Noise Versus Signal4m
Random Walk vs Purely Random Process2m
Time plots, Stationarity, ACV, ACF, Random Walk and MA processes20m
3
완료하는 데 4시간 필요

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 min), 7 readings, 4 quizzes
13개의 동영상
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
7개의 읽기 자료
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
4개 연습문제
Stationarity14m
Series, Backward Shift Operator, Invertibility and Duality30m
AR(p) and the ACF4m
Difference equations and Yule-Walker equations30m
4
완료하는 데 4시간 필요

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 min), 3 readings, 3 quizzes
8개의 동영상
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
3개의 읽기 자료
Partial Autocorrelation and the PACF First Examples10m
Partial Autocorrelation and the PACF: Concept Development10m
All slides together for the next two lessons10m
3개 연습문제
Partial Autocorrelation4m
Yule-Walker in matrix form and Yule-Walker estimation20m
'LakeHuron' dataset40m
4.6
128개의 리뷰Chevron Right

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26%

이 강좌를 통해 확실한 경력상 이점 얻기

최상위 리뷰

대학: 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.

대학: 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.

강사

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Tural Sadigov

Lecturer
Applied Mathematics
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William Thistleton

Associate Professor
Applied Mathematics

뉴욕주립대학교 정보

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....

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 수료증을 구매하면 성적 평가 과제를 포함한 모든 강좌 자료에 접근할 수 있습니다. 강좌를 완료하면 전자 수료증이 성취도 페이지에 추가되며, 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 콘텐츠만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

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