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Martin Burger
R and time series analysis go together hand-in-hand. In this course, you'll learn how to effectively use R and the forecast package to practice time series analysis and work on real-world projects and data.
Read more
R and time series analysis go together hand-in-hand. In this course, you'll learn how to effectively use R and the forecast package to practice time series analysis and work on real-world projects and data.
Read more
R and time series analysis go together hand-in-hand. In this course, you'll learn how to effectively use R and the forecast package to practice time series analysis and work on real-world projects and data.
The R language and software environment are key when producing and analyzing time series data. In this course, Applied Time Series Analysis and Forecasting with R, you’ll learn how to apply modern day time series models on real-world data. First, you'll discover how to design time series models containing trend or seasonality. Next, you'll delve further into models, such as ARIMA, exponential smoothing, and neural networks. Finally, you'll learn how to visualize time series interactively with dygraphs. When you're finished with this course, you'll have the necessary knowledge to apply standard time series models on a univariate time series.
Register for this course and see more details by visiting:
OpenCourser.com/course/ga35rt/applied
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What's inside
Syllabus
Course Overview
Using R for Time Series Analysis
Modeling Unemployment Rates
Forecasting Inflation Rates
Read more
Syllabus
Course Overview
Using R for Time Series Analysis
Modeling Unemployment Rates
Forecasting Inflation Rates
Read more
Predicting Sales Using Neural Networks
Course Summary and Further Learning
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches modern day time series models that increase model accuracy
Suitable for learners interested in working with real-world data
May require prior experience in data analysis and statistics
Helps learners explore advanced time series analysis techniques, beneficial for research
Utilizes the popular R programming language and package
Taught by Martin Burger, a recognized expert in applied time series analysis
Save this course
Save Applied Time Series Analysis and Forecasting with R to your list so you can find it easily later:
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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 Applied Time Series Analysis and Forecasting with R with these
activities:
Review Statistical Analysis
Show steps
Solidify understanding of core statistical concepts and analytical techniques, such as hypothesis testing and linear regression.
Show steps
-
Review notes and assignments from previous statistics courses.
-
Complete practice problems and exercises on statistical concepts.
-
Watch online tutorials or read textbooks to refresh on key statistical methods.
Join a Study Group
Show steps
Collaborate with peers, share knowledge, and gain different perspectives by participating in a study group dedicated to time series analysis.
Show steps
-
Find or create a study group with classmates or fellow learners.
-
Set regular meeting times and establish a clear agenda.
-
Discuss course materials, work on assignments together, and engage in peer-to-peer teaching.
Learn Forecasting with RStudio
Show steps
Gain hands-on experience with RStudio by exploring interactive tutorials that guide you through time series analysis and forecasting techniques.
Show steps
-
Find beginner-friendly RStudio tutorials on time series analysis.
-
Follow along with the tutorials, replicating the code and examples.
-
Attempt the exercises and quizzes provided in the tutorials to test your understanding.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Develop a Time Series Analysis Tutorial
Show steps
Enhance your understanding by creating a tutorial that explains a specific aspect of time series analysis to others.
Show steps
-
Choose a topic that you are comfortable with and that aligns with course objectives.
-
Gather resources and conduct research to ensure accuracy and depth.
-
Create a clear and concise tutorial using text, visuals, and examples.
-
Share your tutorial with classmates or online communities for feedback.
Attend a Time Series Analysis Workshop
Show steps
Expand your knowledge and connect with experts by attending a workshop focused on advanced time series analysis techniques.
Show steps
-
Research and identify relevant time series analysis workshops.
-
Register for the workshop and actively participate in the sessions.
-
Network with other attendees and learn from their experiences.
Analyze Time Series Data
Show steps
Enhance your data analysis skills by practicing with real-world time series datasets and applying different forecasting models.
Show steps
-
Find publicly available time series datasets related to your interests.
-
Use R and the forecast package to explore, visualize, and analyze the data.
-
Experiment with different forecasting models and evaluate their performance.
Time Series Forecasting Project
Show steps
Apply your knowledge by creating a comprehensive project that involves collecting, analyzing, and forecasting a time series dataset of your choice.
Show steps
-
Choose a topic or industry that interests you and find a relevant time series dataset.
-
Use R and the forecast package to analyze and explore the data.
-
Develop and compare different forecasting models to make predictions.
-
Write a report or presentation to showcase your findings and insights.
Review Statistical Analysis
Show steps
Solidify understanding of core statistical concepts and analytical techniques, such as hypothesis testing and linear regression.
Show steps
Show steps
Show steps
- Review notes and assignments from previous statistics courses.
- Complete practice problems and exercises on statistical concepts.
- Watch online tutorials or read textbooks to refresh on key statistical methods.
Join a Study Group
Show steps
Collaborate with peers, share knowledge, and gain different perspectives by participating in a study group dedicated to time series analysis.
Show steps
Show steps
Show steps
- Find or create a study group with classmates or fellow learners.
- Set regular meeting times and establish a clear agenda.
- Discuss course materials, work on assignments together, and engage in peer-to-peer teaching.
Learn Forecasting with RStudio
Show steps
Gain hands-on experience with RStudio by exploring interactive tutorials that guide you through time series analysis and forecasting techniques.
Show steps
Show steps
Show steps
- Find beginner-friendly RStudio tutorials on time series analysis.
- Follow along with the tutorials, replicating the code and examples.
- Attempt the exercises and quizzes provided in the tutorials to test your understanding.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Develop a Time Series Analysis Tutorial
Show steps
Enhance your understanding by creating a tutorial that explains a specific aspect of time series analysis to others.
Show steps
Show steps
Show steps
- Choose a topic that you are comfortable with and that aligns with course objectives.
- Gather resources and conduct research to ensure accuracy and depth.
- Create a clear and concise tutorial using text, visuals, and examples.
- Share your tutorial with classmates or online communities for feedback.
Attend a Time Series Analysis Workshop
Show steps
Expand your knowledge and connect with experts by attending a workshop focused on advanced time series analysis techniques.
Show steps
Show steps
Show steps
- Research and identify relevant time series analysis workshops.
- Register for the workshop and actively participate in the sessions.
- Network with other attendees and learn from their experiences.
Analyze Time Series Data
Show steps
Enhance your data analysis skills by practicing with real-world time series datasets and applying different forecasting models.
Show steps
Show steps
Show steps
- Find publicly available time series datasets related to your interests.
- Use R and the forecast package to explore, visualize, and analyze the data.
- Experiment with different forecasting models and evaluate their performance.
Time Series Forecasting Project
Show steps
Apply your knowledge by creating a comprehensive project that involves collecting, analyzing, and forecasting a time series dataset of your choice.
Show steps
Show steps
Show steps
- Choose a topic or industry that interests you and find a relevant time series dataset.
- Use R and the forecast package to analyze and explore the data.
- Develop and compare different forecasting models to make predictions.
- Write a report or presentation to showcase your findings and insights.
Career center
Learners who complete Applied Time Series Analysis and Forecasting with R will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts use their specialized knowledge in applied mathematics and statistics to design and build data analysis solutions for making better, fact-based decisions. Time series analysis, employing specialized models such as those included in this course, such as ARIMA, exponential smoothing, and neural networks, is a common method used by Data Analysts to analyze and forecast trends in data over time. Those interested in pursuing a career in data analysis will benefit greatly from this course's extensive exploration of these models and techniques.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts apply their skills in mathematics and statistics to analyze financial data and make sound, profitable decisions for investment firms. Time series analysis plays a key role in this, as Quantitative Analysts use it to predict future market trends and behavior. Familiarity with the time series concepts and models covered in this course, such as ARIMA models, exponential smoothing and neural networks, can greatly aid someone interested in becoming a Quantitative Analyst.
Data Scientist
Data Scientist
Data Scientists combine their knowledge of mathematics, statistics, and computer programming to extract insights and create actionable recommendations from data. Time series analysis is a staple technique used by Data Scientists, who employ it to make accurate forecasts and create predictive models for a wide range of applications. This course covers a thorough selection of time series models that can help equip aspiring Data Scientists with the knowledge needed to succeed in this role.
Financial Analyst
Financial Analyst
Financial Analysts use various methods to evaluate the performance of financial assets and make sound investment recommendations. Time series analysis is one of these methods, used by Financial Analysts to identify patterns and forecast trends in financial data. Having a solid foundation in time series concepts and models, such as those taught in this course, is vital for those seeking a career in financial analysis.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning models to solve real-world problems. These models often incorporate time series analysis to make accurate predictions and forecasts. This course provides a detailed look at time series models like ARIMA, exponential smoothing and neural networks, all of which are common tools used by Machine Learning Engineers. Taking this course may be helpful for those aspiring to become a Machine Learning Engineer.
Actuary
Actuary
Actuaries use their knowledge of mathematics, statistics, and finance to assess risk and uncertainty. Time series analysis is an important tool for Actuaries, as it allows them to analyze and predict long-term trends in data. Understanding the concepts and models covered in this course, such as ARIMA, exponential smoothing, and neural networks, can be beneficial for individuals pursuing a career as an Actuary.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use a variety of analytical techniques to improve the efficiency and effectiveness of organizations. Time series analysis is one such technique, used by Operations Research Analysts to analyze data and make informed decisions about resource allocation, scheduling, and other operational aspects. This course offers an in-depth exploration of time series models and techniques, which can aid individuals interested in becoming Operations Research Analysts.
Market Research Analyst
Market Research Analyst
Market Research Analysts gather and interpret data to provide insights into consumer behavior and market trends. Time series analysis is often used by Market Research Analysts to identify trends and forecast future demand for products and services. Taking this course can provide aspiring Market Research Analysts with a solid foundation in time series analysis techniques and models, such as ARIMA, exponential smoothing, and neural networks.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to draw meaningful conclusions. Time series analysis is a specialized field within statistics, concerned with analyzing data that is collected over time. This course covers a range of time series models, including ARIMA, exponential smoothing, and neural networks, which are essential for individuals pursuing a career as a Statistician.
Business Analyst
Business Analyst
Business Analysts use data to analyze and solve business problems. Time series analysis can be a valuable tool for Business Analysts, as it allows them to identify trends and make predictions about future business performance. This course covers a range of time series models, including ARIMA, exponential smoothing, and neural networks, which can help individuals interested in becoming Business Analysts gain the necessary skills and knowledge.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. While not a direct requirement, knowledge of time series analysis can be beneficial for Software Engineers working on projects that involve data analysis and forecasting. This course covers a range of time series models, including ARIMA, exponential smoothing, and neural networks, which can be useful for Software Engineers looking to expand their skillset.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines and infrastructure. While not a direct requirement, knowledge of time series analysis can be beneficial for Data Engineers working on projects that involve time-series data. This course covers a range of time series models, including ARIMA, exponential smoothing, and neural networks, which can be useful for Data Engineers looking to expand their skillset.
Product Manager
Product Manager
Product Managers oversee the development and launch of products. While not a direct requirement, knowledge of time series analysis can be beneficial for Product Managers working on products that involve time-series data or forecasting. This course covers a range of time series models, including ARIMA, exponential smoothing, and neural networks, which can be useful for Product Managers looking to expand their understanding of data analysis techniques.
Marketing Manager
Marketing Manager
Marketing Managers plan and execute marketing campaigns to promote products and services. While not a direct requirement, knowledge of time series analysis can be beneficial for Marketing Managers working on campaigns that involve time-series data or forecasting. This course covers a range of time series models, including ARIMA, exponential smoothing, and neural networks, which can be useful for Marketing Managers looking to expand their understanding of data analysis techniques.
Financial Advisor
Financial Advisor
Financial Advisors provide financial advice and guidance to individuals and businesses. While not a direct requirement, knowledge of time series analysis can be beneficial for Financial Advisors working with clients who have investments or other financial products that involve time-series data. This course covers a range of time series models, including ARIMA, exponential smoothing, and neural networks, which can be useful for Financial Advisors looking to expand their understanding of data analysis techniques.
For more career information including salaries, visit:
OpenCourser.com/course/ga35rt/applied
Reading list
We've selected ten 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
Applied Time Series Analysis and Forecasting with R.
Save
Offers comprehensive coverage of forecasting time series and provides a dedicated chapter on forecasting using machine learning which is especially relevant to the current course.
Includes a broad range of examples to provide you with a solid theoretical background in time series analysis and forecasting. Anderson-Cook covers fundamental topics including stationary time series, nonstationary time series, and structural time series models with a view towards modern-day applications.
Save
Introduces you to the application of time series analysis in R. It contains intuitive explanations and practical examples utilizing real-world data throughout.
Is written in a clear and concise style and provides numerous examples to help you understand the application of time series analysis. Specifically, its coverage of the Box-Jenkins approach complements the neural network methods covered in the current course.
Provides a comprehensive treatment of robust time series analysis techniques. It covers a wide range of topics, from the basics to more advanced topics.
Presents cutting-edge topics such as adaptive filtering, bootstrap methods, multiple time series analysis, and nonlinear models in a cohesive manner which complements the existing course materials.
Save
Classic text on time series analysis. It provides a comprehensive overview of the field, from the basics to more advanced topics. This book is quite technical and provides a valuable historical background but has less focus on modern approaches than other texts.
Provides a comprehensive introduction to time series analysis in Spanish. It covers a wide range of topics, from the basics to more advanced topics.
Provides a practical guide to time series analysis in Spanish using R. It covers a wide range of topics, from the basics to more advanced topics.
Is an excellent resource for anyone interested in multivariate time series analysis in R. It covers a wide range of topics, including vector autoregressive (VAR) models, state space models, and dynamic factor models.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ga35rt/applied
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Time
123 hours
Level
Introductory
Via
Pluralsight
Instructor
Martin Burger
Language
English
Good to know
Teaches modern day time series models that increase model accuracy
Suitable for learners interested in working with real-world data
May require prior experience in data analysis and statistics
Helps learners explore advanced time series analysis techniques, beneficial for research
Utilizes the popular R programming language and package
Taught by Martin Burger, a recognized expert in applied time series analysis
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