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Introduction to Time Series

This course introduces basic time series analysis and forecasting methods. Topics include stationary processes, ARMA models, modeling and forecasting using ARMA models, nonstationary and seasonal time series models, state-space models, and forecasting techniques.

By the end of this course, students will be able to:

- Describe important time series models and their applications in various fields.

- Formulate real life problems using time series models.

- Use statistical software to estimate models from real data and draw conclusions and develop solutions from the estimated models.

By the end of this course, students will be able to:

- Describe important time series models and their applications in various fields.

- Formulate real life problems using time series models.

- Use statistical software to estimate models from real data and draw conclusions and develop solutions from the estimated models.

- Use visual and numerical diagnostics to assess the soundness of their models.

- Communicate the statistical analyses of substantial data sets through explanatory text, tables, and graphs.

- Combine and adapt different statistical models to analyze larger and more complex data.

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What's inside

Syllabus

Module 1: Course Introduction and Intuition for Stationarity

Welcome to Introduction to Time Series! This module introduces students to the foundational concepts and tools for time series analysis, equipping them with the necessary skills to understand, model, and analyze data that change over time. Through a blend of theoretical lessons and practical exercises, students will explore the nature of time series data, the principles of stationarity, and begin their journey into time series modeling.

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Develops core skills and professional expertise in time series analysis, valuable in industries requiring forecasting and predictive analytics

Taught by Trevor Leslie, a recognized expert in time series analysis, providing learners with access to industry-leading knowledge

Provides a strong foundation in time series concepts, statistical modeling, and forecasting techniques, preparing learners for advanced studies in data science

Requires access to statistical software like R, potentially introducing a barrier for learners without prior experience

Focuses on theoretical concepts and statistical modeling, with limited emphasis on practical applications

Covers a wide range of time series models, but may require additional resources for in-depth understanding of specific models

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Reviews summary

Foundational time series with r

According to learners, this course offers a phenomenal introduction to time series analysis, providing a strong theoretical foundation and delving deep into mathematical underpinnings. Many commend the instructor's clear explanations, making complex concepts like stationarity, ARMA, ACF, and PACF intuitive. While the course includes practical R labs that solidify understanding, some students seeking extensive real-world case studies or advanced data manipulation might find the approach too academic or desire more hands-on projects beyond the provided exercises. It’s ideal for beginners with a solid grasp of statistics.

Integrates R, but some desire more extensive practical projects.

"The R labs are practical and truly help solidify the theoretical concepts."

"I wish there were more real-world case studies or projects beyond just the R exercises. Felt a bit too academic for my professional needs."

"While the R examples are helpful, they are mostly illustrative and don't involve complex data manipulation, so be prepared to explore more on your own."

Best for those with basic statistical and mathematical skills.

"I struggled a lot with this course. It assumes a very strong mathematical background, which I lacked. The pace was too fast for me..."

"As a beginner to time series but with a good grasp of statistics, this course was perfect."

"The assignments are challenging but fair, pushing you to apply what you learn."

Concepts build progressively and cover essential topics.

"I loved how the concepts built on each other module by module."

"The progression from basic concepts to ARIMA/SARIMA models was logical and well-structured."

"Comprehensive overview of time series. The lectures are detailed and the syllabus covers all the essential topics for an introduction."

Complex topics are explained intuitively by the instructor.

"The instructor makes complex topics like stationarity and ARMA models incredibly clear and easy to grasp."

"The explanations of ACF, PACF, and their use in model identification are top-notch."

"The clarity of explanations for complex functions like ACF/PACF is unmatched."

Delivers deep conceptual and mathematical understanding.

"This course offers a strong theoretical foundation, delving deep into the mathematical underpinnings of time series."

"I gained a deep conceptual understanding that I couldn't get from just tutorials."

"It's an academic approach, which is good for a solid understanding..."

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 Introduction to Time Series with these activities:

Organize your notes, assignments, quizzes, and exams

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Organizing your course materials will help you to stay on track and make it easier to find the information you need. This activity will help you to be more efficient and productive in your studies.

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Gather all of your course materials.
Create a system for organizing your materials.

Review linear algebra and calculus

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Linear algebra and calculus are essential prerequisites for time series analysis. This activity will help you to refresh your skills in these areas and ensure that you are prepared for the course.

Browse courses on Linear Algebra

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Review your notes from linear algebra and calculus.
Take a practice quiz or exam.

Watch video tutorials on time series analysis

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Video tutorials can be a great way to learn new concepts or review material. This activity will help you to supplement the material covered in the course and gain a deeper understanding of time series analysis.

Browse courses on Time Series Analysis

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Find a set of video tutorials on time series analysis.
Watch the tutorials.
Take notes on the most important concepts.

Five other activities

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Show all eight activities

Read the book Time Series Analysis by Forecasting by Robert Shumway and David Stoffer

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This book is essential reading for anyone who wants to understand time series analysis and forecasting. It provides a comprehensive overview of the field, from the basics to the most advanced topics.

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Read the book carefully.
Take notes on the most important concepts.
Work through the exercises at the end of each chapter.

Practice solving time series analysis problems

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Solving problems is a great way to test your understanding of the material and build your skills. This activity will help you to identify areas where you need more practice.

Browse courses on Time Series Analysis

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Find a set of time series analysis problems.
Solve the problems.
Check your answers.

Join a study group or online forum

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Discussing the material with other students can help you to understand the concepts more deeply and identify areas where you need more practice.

Browse courses on Time Series Analysis

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Find a study group or online forum.
Participate in discussions.

Build a time series forecasting model for a real-world dataset

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This project will allow you to apply the concepts you learn in the course to a real-world problem. You will also gain valuable experience in data analysis and model building.

Browse courses on Time Series Forecasting

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Gather a real-world dataset.
Explore the data and identify the time series.
Build a time series forecasting model.
Evaluate the performance of the model.
Write a report on your findings.

Write a blog post or article on a topic related to time series analysis

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Writing a blog post or article will help you to solidify your understanding of the material and share your knowledge with others. This activity will also help you to develop your communication and writing skills.

Browse courses on Time Series Analysis

Show steps

Choose a topic related to time series analysis.
Write a blog post or article on the topic.
Publish your blog post or article.

Career center

Learners who complete Introduction to Time Series will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 50 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 Introduction to Time Series.

Time Series Analysis

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This seminal work by the pioneers of ARMA modeling provides a comprehensive treatment of the Box-Jenkins methodology for time series analysis. It must-read for anyone interested in the historical development and theoretical foundations of ARMA models.

Time Series Analysis: Forecasting and Control...

Hardcover

$$$$

Time Series Analysis: Forecasting and Control...

Kindle Edition

$$$$

Forecasting: principles and practice

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Provides a comprehensive overview of forecasting methods, including SARIMA models. It is written by two of the leading experts in the field, and it is essential reading for anyone who wants to learn about forecasting.

Introduction to Time Series

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What's inside

Syllabus

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Reviews summary

Foundational time series with r

Activities

Career center

Reading list

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