University of California, Santa Cruz
Bayesian Statistics: Time Series Analysis
University of California, Santa Cruz

Bayesian Statistics: Time Series Analysis

This course is part of Bayesian Statistics Specialization

Taught in English

Some content may not be translated

Raquel Prado

Instructor: Raquel Prado

4,271 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.2

(12 reviews)

Intermediate level

Recommended experience

22 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Build models that describe temporal dependencies.

  • Use R for analysis and forecasting of times series.

  • Explain stationary time series processes.

Details to know

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Assessments

10 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.2

(12 reviews)

Intermediate level

Recommended experience

22 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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This course is part of the Bayesian Statistics Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 5 modules in this course

This module defines stationary time series processes, the autocorrelation function and the autoregressive process of order one or AR(1). Parameter estimation via maximum likelihood and Bayesian inference in the AR(1) are also discussed.

What's included

9 videos12 readings4 quizzes1 peer review

This module extends the concepts learned in Week 1 about the AR(1) process to the general case of the AR(p). Maximum likelihood estimation and Bayesian posterior inference in the AR(p) are discussed.

What's included

9 videos8 readings2 quizzes1 peer review

Normal Dynamic Linear Models (NDLMs) are defined and illustrated in this module using several examples. Model building based on the forecast function via the superposition principle is explained. Methods for Bayesian filtering, smoothing and forecasting for NDLMs in the case of known observational variances and known system covariance matrices are discussed and illustrated.

What's included

10 videos7 readings2 quizzes1 peer review

What's included

7 videos4 readings2 quizzes1 peer review

In this final project you will use normal dynamic linear models to analyze a time series dataset downloaded from Google trend.

What's included

1 peer review

Instructor

Instructor ratings
4.5 (6 ratings)
Raquel Prado
University of California, Santa Cruz
1 Course4,271 learners

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Recommended if you're interested in Probability and Statistics

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4.2

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5

Reviewed on Feb 5, 2024

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