This course for practicing and aspiring data scientists and statisticians. It is the fourth of a four-course sequence introducing the fundamentals of Bayesian statistics. It builds on the course Bayesian Statistics: From Concept to Data Analysis, Techniques and Models, and Mixture models.
Bayesian Statistics: Time Series Analysis
Bayesian Statistics: Time Series Analysis
This course is part of Bayesian Statistics Specialization
Instructor: Raquel Prado
4,742 already enrolled
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Gain insight into a topic and learn the fundamentals.
4.3
(15 reviews)
Intermediate level
Recommended experience
22 hours to complete
3 weeks at 7 hours a week
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 assignments
Taught in English
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This course is part of the Bayesian Statistics 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 assignments1 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 assignments1 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 assignments1 peer review
What's included
7 videos4 readings2 assignments1 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
4.4 (7 ratings)Offered by
Recommended if you're interested in Probability and Statistics
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4.3
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5
Reviewed on Feb 5, 2024
It was a nice course, but it would be better if there were more supplementary materials for the proof and theoretical discussion.
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