We may earn an affiliate commission when you visit our partners.
Raquel Prado
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.
Read more
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.
Read more
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.
Time series analysis is concerned with modeling the dependency among elements of a sequence of temporally related variables. To succeed in this course, you should be familiar with calculus-based probability, the principles of maximum likelihood estimation, and Bayesian inference. You will learn how to build models that can describe temporal dependencies and how to perform Bayesian inference and forecasting for the models. You will apply what you've learned with the open-source, freely available software R with sample databases. Your instructor Raquel Prado will take you from basic concepts for modeling temporally dependent data to implementation of specific classes of models
Register for this course and see more details by visiting:
OpenCourser.com/course/ox8h44/bayesian
What's inside
Syllabus
Week 1: Introduction to time series and the AR(1) process
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.
Read more
Syllabus
Week 1: Introduction to time series and the AR(1) process
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.
Read more
Week 2: The AR(p) process
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.
Week 3: Normal dynamic linear models, Part I
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.
Week 4: Normal dynamic linear models, Part II
Week 5: Final Project
In this final project you will use normal dynamic linear models to analyze a time series dataset downloaded from Google trend.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores time series analysis, a technique used in fields like econometrics, finance, and signal processing
Provides a solid foundation for learners interested in building models for temporally dependent data
Uses the R programming language with sample databases for practical application
Taught by Raquel Prado, an expert in Bayesian statistics and time series analysis
Requires familiarity with calculus-based probability, maximum likelihood estimation, and Bayesian inference
Save this course
Save Bayesian Statistics: Time Series Analysis to your list so you can find it easily later:
Save
Save
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Bayesian Statistics: Time Series Analysis with these
activities:
Review class syllabus and course readings
Show steps
Provides a foundational knowledge of the course structure and materials, promoting familiarity and comprehension of the course content.
Browse courses on
Time Series Analysis
Show steps
-
Read the course syllabus and note key course components, goals, and deadlines.
-
Review the required readings and note important concepts and ideas.
Attend a webinar or conference on time series analysis
Show steps
Provides an opportunity to learn from experts, network with professionals, and gain insights into current practices.
Browse courses on
Time Series Analysis
Show steps
-
Research upcoming webinars or conferences related to time series analysis.
-
Register for and attend the event.
-
Actively participate in discussions and networking opportunities.
Practice calculating autocorrelations
Show steps
Reinforces understanding of autocorrelation, a fundamental concept in time series analysis.
Browse courses on
Time Series Analysis
Show steps
-
Calculate the autocorrelation function for a given time series dataset.
-
Interpret the results to understand the temporal dependencies in the data.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore R packages for time series analysis
Show steps
Familiarizes students with the available R packages and their functionality for analyzing time series data.
Browse courses on
R Programming
Show steps
-
Install and load the recommended R packages.
-
Follow tutorials or documentation to learn about the package functions.
-
Practice using the package functions on sample datasets.
Create a visual representation of a time series
Show steps
Enhances comprehension of time series patterns and trends by visualizing them.
Show steps
-
Select an appropriate time series dataset.
-
Choose and use a visualization tool to create a graph or chart.
-
Analyze the visualization to identify patterns and trends.
Conduct a peer review of time series analysis models
Show steps
Encourages critical thinking and improves understanding of model selection and evaluation techniques.
Browse courses on
Time Series Analysis
Show steps
-
Develop a time series model for a given dataset.
-
Exchange models with peers and provide constructive feedback.
-
Analyze the feedback received and revise the model accordingly.
Develop a model for forecasting time series data
Show steps
Applies knowledge of time series models and Bayesian inference to make predictions about future data points.
Browse courses on
Time Series Forecasting
Show steps
-
Select an appropriate time series model based on the data characteristics.
-
Estimate the model parameters using Bayesian inference.
-
Evaluate the model's performance on a holdout dataset.
-
Use the model to forecast future data points.
Contribute to an open-source time series analysis project
Show steps
Provides practical experience in applying time series analysis methods and contributing to the wider community.
Browse courses on
Time Series Analysis
Show steps
-
Identify an open-source time series analysis project to contribute to.
-
Review the project's documentation and codebase.
-
Identify a task or feature to work on.
-
Implement the task or feature and submit a pull request.
Review class syllabus and course readings
Show steps
Provides a foundational knowledge of the course structure and materials, promoting familiarity and comprehension of the course content.
Browse courses on
Time Series Analysis
Show steps
Show steps
Show steps
- Read the course syllabus and note key course components, goals, and deadlines.
- Review the required readings and note important concepts and ideas.
Attend a webinar or conference on time series analysis
Show steps
Provides an opportunity to learn from experts, network with professionals, and gain insights into current practices.
Browse courses on
Time Series Analysis
Show steps
Show steps
Show steps
- Research upcoming webinars or conferences related to time series analysis.
- Register for and attend the event.
- Actively participate in discussions and networking opportunities.
Practice calculating autocorrelations
Show steps
Reinforces understanding of autocorrelation, a fundamental concept in time series analysis.
Browse courses on
Time Series Analysis
Show steps
Show steps
Show steps
- Calculate the autocorrelation function for a given time series dataset.
- Interpret the results to understand the temporal dependencies in the data.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore R packages for time series analysis
Show steps
Familiarizes students with the available R packages and their functionality for analyzing time series data.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Install and load the recommended R packages.
- Follow tutorials or documentation to learn about the package functions.
- Practice using the package functions on sample datasets.
Create a visual representation of a time series
Show steps
Enhances comprehension of time series patterns and trends by visualizing them.
Show steps
Show steps
Show steps
- Select an appropriate time series dataset.
- Choose and use a visualization tool to create a graph or chart.
- Analyze the visualization to identify patterns and trends.
Conduct a peer review of time series analysis models
Show steps
Encourages critical thinking and improves understanding of model selection and evaluation techniques.
Browse courses on
Time Series Analysis
Show steps
Show steps
Show steps
- Develop a time series model for a given dataset.
- Exchange models with peers and provide constructive feedback.
- Analyze the feedback received and revise the model accordingly.
Develop a model for forecasting time series data
Show steps
Applies knowledge of time series models and Bayesian inference to make predictions about future data points.
Browse courses on
Time Series Forecasting
Show steps
Show steps
Show steps
- Select an appropriate time series model based on the data characteristics.
- Estimate the model parameters using Bayesian inference.
- Evaluate the model's performance on a holdout dataset.
- Use the model to forecast future data points.
Contribute to an open-source time series analysis project
Show steps
Provides practical experience in applying time series analysis methods and contributing to the wider community.
Browse courses on
Time Series Analysis
Show steps
Show steps
Show steps
- Identify an open-source time series analysis project to contribute to.
- Review the project's documentation and codebase.
- Identify a task or feature to work on.
- Implement the task or feature and submit a pull request.
Career center
Learners who complete Bayesian Statistics: Time Series Analysis will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use statistical and computational methods to extract insights from data. They work in a variety of industries, including technology, healthcare, finance, and retail. This course could be useful for Data Scientists who want to learn more about time series analysis. The course covers topics such as AR(1) and AR(p) processes, normal dynamic linear models, and Bayesian filtering and smoothing. This knowledge could help Data Scientists develop and implement more effective machine learning models for time series data.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They work in a variety of financial institutions, including investment banks, hedge funds, and asset management firms. This course could be useful for Quantitative Analysts who want to learn more about time series analysis. The course covers topics such as AR(1) and AR(p) processes, normal dynamic linear models, and Bayesian filtering and smoothing. This knowledge could help Quantitative Analysts develop and implement more effective financial models.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment recommendations. They work in a variety of financial institutions, including investment banks, brokerages, and asset management firms. This course could be useful for Financial Analysts who want to learn more about time series analysis. The course covers topics such as AR(1) and AR(p) processes, normal dynamic linear models, and Bayesian filtering and smoothing. This knowledge could help Financial Analysts develop and implement more effective financial models.
Statistician
Statistician
Statisticians apply statistical methods to collect, analyze, interpret, and present data. They work in a variety of industries, including healthcare, finance, marketing, and government. This course could be useful for Statisticians who want to learn more about time series analysis. The course covers topics such as AR(1) and AR(p) processes, normal dynamic linear models, and Bayesian filtering and smoothing. This knowledge could help Statisticians develop and implement more effective statistical models for time series data.
Actuary
Actuary
Actuaries use mathematics and statistics to assess risk and uncertainty. They work in a variety of industries, including insurance, healthcare, and finance. This course could be useful for Actuaries who want to learn more about time series analysis. The course covers topics such as AR(1) and AR(p) processes, normal dynamic linear models, and Bayesian filtering and smoothing. This knowledge could help Actuaries develop and implement more effective risk models.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve business problems. They work in a variety of industries, including manufacturing, logistics, and healthcare. This course could be useful for Operations Research Analysts who want to learn more about time series analysis. The course covers topics such as AR(1) and AR(p) processes, normal dynamic linear models, and Bayesian filtering and smoothing. This knowledge could help Operations Research Analysts develop and implement more effective operations research models.
Risk Manager
Risk Manager
Risk Managers use statistical and financial models to assess and manage risk. They work in a variety of industries, including banking, insurance, and healthcare. This course could be useful for Risk Managers who want to learn more about time series analysis. The course covers topics such as AR(1) and AR(p) processes, normal dynamic linear models, and Bayesian filtering and smoothing. This knowledge could help Risk Managers develop and implement more effective risk management models.
Economist
Economist
Economists use economic theory and data to analyze economic issues. They work in a variety of industries, including government, academia, and business. This course could be useful for Economists who want to learn more about time series analysis. The course covers topics such as AR(1) and AR(p) processes, normal dynamic linear models, and Bayesian filtering and smoothing. This knowledge could help Economists develop and implement more effective economic models.
Biostatistician
Biostatistician
Biostatisticians use statistical methods to analyze biological data. They work in a variety of industries, including healthcare, pharmaceuticals, and academia. This course could be useful for Biostatisticians who want to learn more about time series analysis. The course covers topics such as AR(1) and AR(p) processes, normal dynamic linear models, and Bayesian filtering and smoothing. This knowledge could help Biostatisticians develop and implement more effective statistical models for biological data.
Market Researcher
Market Researcher
Market Researchers use statistical methods to collect and analyze data about consumers and markets. They work in a variety of industries, including marketing, advertising, and product development. This course could be useful for Market Researchers who want to learn more about time series analysis. The course covers topics such as AR(1) and AR(p) processes, normal dynamic linear models, and Bayesian filtering and smoothing. This knowledge could help Market Researchers develop and implement more effective research methods.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning models. They work in a variety of industries, including technology, finance, and healthcare. This course could be useful for Machine Learning Engineers who want to learn more about time series analysis. Time-series data is often used in machine learning applications, such as predicting stock prices and detecting fraud. This knowledge could help Machine Learning Engineers develop and implement more effective machine learning models.
Data Analyst
Data Analyst
Data Analysts use statistical and computational methods to analyze data and derive insights. They work in a variety of industries, including technology, finance, and healthcare. This course could be useful for Data Analysts who want to learn more about time series analysis. Time-series data is often used in data analytics applications, such as forecasting demand and predicting customer behavior. This knowledge could help Data Analysts develop and implement more effective data analytics solutions.
Financial Risk Manager
Financial Risk Manager
Financial Risk Managers use statistical and financial models to assess and manage financial risk. They work in a variety of financial institutions, including banks, hedge funds, and asset management firms. This course could be useful for Financial Risk Managers who want to learn more about time series analysis. Time-series data is often used in financial risk management applications, such as modeling market volatility and predicting credit risk. This knowledge could help Financial Risk Managers develop and implement more effective financial risk management models.
Software Engineer
Software Engineer
Software Engineers design, develop, and test software systems. They work in a variety of industries, including technology, finance, and healthcare. This course could be useful for Software Engineers who want to learn more about time series analysis. Time-series data is often used in software applications, such as monitoring system performance and detecting anomalies. This knowledge could help Software Engineers develop and implement more effective software systems.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers design and develop computer systems that can interpret visual information. They work in a variety of industries, including robotics, autonomous vehicles, and medical imaging. A course in time series analysis could be useful for Computer Vision Engineers who want to learn more about how to model and analyze time-series data. Time-series data is often used in computer vision applications, such as tracking objects in videos and recognizing gestures. This knowledge could help Computer Vision Engineers develop and implement more effective computer vision systems.
For more career information including salaries, visit:
OpenCourser.com/course/ox8h44/bayesian
Reading list
We've selected eight books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Bayesian Statistics: Time Series Analysis.
Covers a wide range of time series analysis techniques, from basic to advanced, and it valuable reference for practitioners in various fields.
Provides a comprehensive introduction to Bayesian statistics, using R and Stan, and it valuable resource for students and practitioners alike.
Save
Provides a comprehensive overview of forecasting techniques, covering both theoretical and practical aspects.
Save
Provides a comprehensive introduction to time series econometrics, covering both theoretical and practical aspects.
Save
Classic text on time series analysis, covering a wide range of topics from basic concepts to advanced techniques.
Provides a clear and concise introduction to time series analysis and forecasting, making it a valuable resource for students and practitioners alike.
Provides a comprehensive introduction to Bayesian analysis for social scientists, covering both theoretical and practical aspects.
Save
Provides a practical introduction to time series analysis, using real-world examples and the R software.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ox8h44/bayesian
Share
Similar courses
Here are nine courses similar to
Bayesian Statistics: Time Series Analysis.
Bayesian Statistics: Techniques and Models
OpenCourser.com/course/1lplko/bayesian
Bayesian Statistics: Techniques and Models
Most relevant
Bayesian Statistics
OpenCourser.com/course/uia8ne/bayesian
Bayesian Statistics
Most relevant
Advanced Bayesian Statistics Using R
OpenCourser.com/course/9hvbgg/advanced
Advanced Bayesian Statistics Using R
Most relevant
Introduction to Bayesian Statistics
OpenCourser.com/course/kge58f/introduction
Introduction to Bayesian Statistics
Most relevant
Introduction to Bayesian Statistics Using R
OpenCourser.com/course/5azi8b/introduction
Introduction to Bayesian Statistics Using R
Most relevant
Fitting Statistical Models to Data with Python
OpenCourser.com/course/8fwgeo/fitting
Fitting Statistical Models to Data with Python
Most relevant
Probability - The Science of Uncertainty and Data
OpenCourser.com/course/dldr0j/probability
Probability - The Science of Uncertainty and Data
Most relevant
Applied Bayesian for Analytics
OpenCourser.com/course/wbc2f1/applied
Applied Bayesian for Analytics
Most relevant
Bayesian Statistics: Mixture Models
OpenCourser.com/course/pw6r49/bayesian
Bayesian Statistics: Mixture Models
Most relevant
Level
Intermediate
Via
Coursera
Institution
University of California, Santa Cruz
Instructor
Raquel Prado
Language
English
Transcripts
Español
Français
Português
Deutsch
Italiano
中文
العربية
한국어
日本語
हिंदी
Nederlands
Svenska
Pусский
Türkçe
Polski
Ελληνικά
українська мова
Bahasa Indonesia
ไทย
қазақша
This course belongs to a
collection
called
Bayesian Statistics. It's comprised of five courses:
- Bayesian Statistics: Mixture Models
- Bayesian Statistics: Time Series Analysis (viewing)
- Bayesian Statistics: Techniques and Models
- Bayesian Statistics: From Concept to Data Analysis
- Bayesian Statistics: Capstone Project
Good to know
Explores time series analysis, a technique used in fields like econometrics, finance, and signal processing
Provides a solid foundation for learners interested in building models for temporally dependent data
Uses the R programming language with sample databases for practical application
Taught by Raquel Prado, an expert in Bayesian statistics and time series analysis
Requires familiarity with calculus-based probability, maximum likelihood estimation, and Bayesian inference
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser