Informações sobre o curso

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Prazos flexíveis
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Nível intermediário
Aprox. 22 horas para completar
Inglês
Legendas: Inglês

Habilidades que você terá

Time Series ForecastingTime SeriesTime Series Models

Resultados de carreira do aprendiz

36%

comecei uma nova carreira após concluir estes cursos

26%

consegui um benefício significativo de carreira com este curso
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 22 horas para completar
Inglês
Legendas: Inglês

oferecido por

Logotipo de Universidade Estadual de Nova York

Universidade Estadual de Nova York

Programa - O que você aprenderá com este curso

Classificação do conteúdoThumbs Up94%(5,598 classificações)Info
Semana
1

Semana 1

3 horas para concluir

WEEK 1: Basic Statistics

3 horas para concluir
12 vídeos (Total 79 mín.), 4 leituras, 2 testes
12 videos
Week 1 Welcome Video3min
Getting Started in R: Download and Install R on Windows5min
Getting Started in R: Download and Install R on Mac2min
Getting Started in R: Using Packages7min
Concatenation, Five-number summary, Standard Deviation5min
Histogram in R6min
Scatterplot in R3min
Review of Basic Statistics I - Simple Linear Regression6min
Reviewing Basic Statistics II More Linear Regression8min
Reviewing Basic Statistics III - Inference12min
Reviewing Basic Statistics IV9min
4 leituras
Welcome to Week 11min
Getting Started with R10min
Basic Statistics Review (with linear regression and hypothesis testing)10min
Measuring Linear Association with the Correlation Function10min
2 exercícios práticos
Visualization4min
Basic Statistics Review18min
Semana
2

Semana 2

2 horas para concluir

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

2 horas para concluir
10 vídeos (Total 54 mín.), 1 leitura, 3 testes
10 videos
Introduction1min
Time plots8min
First Intuitions on (Weak) Stationarity2min
Autocovariance function9min
Autocovariance coefficients6min
Autocorrelation Function (ACF)5min
Random Walk9min
Introduction to Moving Average Processes3min
Simulating MA(2) process6min
1 leituras
All slides together for the next two lessons10min
3 exercícios práticos
Noise Versus Signal4min
Random Walk vs Purely Random Process2min
Time plots, Stationarity, ACV, ACF, Random Walk and MA processes20min
Semana
3

Semana 3

4 horas para concluir

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

4 horas para concluir
13 vídeos (Total 112 mín.), 7 leituras, 4 testes
13 videos
Stationarity - Intuition and Definition13min
Stationarity - First Examples...White Noise and Random Walks9min
Stationarity - First Examples...ACF of Moving Average10min
Series and Series Representation8min
Backward shift operator5min
Introduction to Invertibility12min
Duality9min
Mean Square Convergence (Optional)7min
Autoregressive Processes - Definition, Simulation, and First Examples9min
Autoregressive Processes - Backshift Operator and the ACF10min
Difference equations7min
Yule - Walker equations6min
7 leituras
Stationarity - Examples -White Noise, Random Walks, and Moving Averages10min
Stationarity - Intuition and Definition10min
Stationarity - ACF of a Moving Average10min
All slides together for lesson 2 and 410min
Autoregressive Processes- Definition and First Examples10min
Autoregressive Processes - Backshift Operator and the ACF10min
Yule - Walker equations - Slides10min
4 exercícios práticos
Stationarity14min
Series, Backward Shift Operator, Invertibility and Duality30min
AR(p) and the ACF4min
Difference equations and Yule-Walker equations30min
Semana
4

Semana 4

4 horas para concluir

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

4 horas para concluir
8 vídeos (Total 69 mín.), 3 leituras, 3 testes
8 videos
Partial Autocorrelation and the PACF First Examples10min
Partial Autocorrelation and the PACF - Concept Development8min
Yule-Walker Equations in Matrix Form8min
Yule Walker Estimation - AR(2) Simulation17min
Yule Walker Estimation - AR(3) Simulation5min
Recruitment data - model fitting8min
Johnson & Johnson-model fitting8min
3 leituras
Partial Autocorrelation and the PACF First Examples10min
Partial Autocorrelation and the PACF: Concept Development10min
All slides together for the next two lessons10min
3 exercícios práticos
Partial Autocorrelation4min
Yule-Walker in matrix form and Yule-Walker estimation20min
'LakeHuron' dataset40min

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