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Introduction to Time Series

Trevor Leslie

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.

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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.

- 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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Module 2: Basic Analysis of Stationary Processes
This module offers a comprehensive exploration of ARMA (Auto Regressive Moving Average) processes, equipping students with the ability to dissect and comprehend the mechanics of ARMA models, including terminology and mathematical foundations like the backward shift operator. Through hands-on experience, students will learn to classify ARMA processes based on causality and invertibility, estimate key statistical properties of stationary time series such as the sample mean, autocovariance, and autocorrelation using R, and employ visual tools to determine the order of MA processes. This module also provides an introduction to forecasting, where students apply prediction operators to achieve optimal forecasts for stationary processes, rounding out their foundational understanding of time series analysis.
Module 3: ARMA processes and their Autocorrelation Functions
This module delves into advanced concepts surrounding ARMA(p,q) processes, focusing on the intricacies of causality and invertibility. Students will gain the analytical skills necessary to categorize ARMA processes, enhancing their understanding through the computation and interpretation of the Partial Autocorrelation Function (PACF) for both theoretical processes and real time series data using R. This module emphasizes the practical application of ACF and PACF plots in determining the order of ARMA processes and it introduces advanced forecasting techniques, providing students with the tools to implement precise forecasting methods for a variety of ARMA models, preparing them for complex challenges in time series analysis.
Module 4: More About the ACF; Best Linear Predictors, Autocorrelation, and Partial Autocorrelation
In this module, students will continue to delve into the intricate world of time series analysis, exploring techniques for parameter estimation and model selection. Students will master the Yule-Walker equations, a fundamental tool for preliminary parameter estimation, and gain proficiency in maximum likelihood estimation for ARMA processes. Additionally, students will learn to determine the optimal model order through various statistical criteria, ensuring parsimonious yet accurate representations of time series data.
Module 5: Fitting Data to ARMA models
This module equips students with advanced techniques for modeling and forecasting time series data. Students will explore the ARIMA framework, learning how to handle trends and seasonality through differencing and seasonal components. They will master the interpretation of ACF and PACF plots, enabling them to determine appropriate model orders. Additionally, students will explore the intricacies of SARIMA processes, incorporating both seasonal and non-seasonal components. This module culminates with forecasting techniques tailored for ARIMA and SARIMA models, empowering students to make accurate predictions for complex time series scenarios.
Module 6: Diagnostics and Order Selection
This module focuses on refining ARMA model selection and diagnostics, teaching students to critically evaluate model fit using standardized residuals and various diagnostic plots in R, including the normal Q-Q plot and the Ljung-Box test. Additionally, the module covers the principles of model order selection, emphasizing the avoidance of overfitting and the application of the Akaike Information Criterion (AIC) and its correction (AICC) in choosing the optimal ARMA model for specific time series data.
Module 7: Nonstationary processes: ARIMA and SARIMA Models
This module introduces students to ARIMA and SARIMA modeling techniques, essential for analyzing non-stationary and seasonal time series data. In the first lesson, students will learn to define ARIMA processes, use the Dickey-Fuller test to determine the need for differencing, and fit ARIMA models using R, incorporating the concept of exponential smoothing. The second lesson extends these skills to SARIMA models, focusing on identifying seasonality and fitting these models to capture seasonal patterns in data, providing a comprehensive toolkit for sophisticated time series analysis.
Module 8: More on Forecasting
This module equips students with advanced forecasting techniques beyond one-step-ahead predictions, focusing on ARMA, ARIMA, and SARIMA processes using R. In the initial lesson, learners will explore methodologies for generating multi-step forecasts with these models. The subsequent lesson dives into exponential smoothing, teaching students to handle models with additive errors, trends, and seasonality, and how to effectively use R's HoltWinters and forecast functions to fit and interpret time series forecasts, thus providing a thorough grounding in dynamic forecasting methods.
Summative Course Assessment
This module contains the summative course assessment that has been designed to evaluate your understanding of the course material and assess your ability to apply the knowledge you have acquired throughout the course.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
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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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
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.
Show steps
  • Gather all of your course materials.
  • Create a system for organizing your materials.
Review linear algebra and calculus
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
Show steps
  • Review your notes from linear algebra and calculus.
  • Take a practice quiz or exam.
Watch video tutorials on time series analysis
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
Show steps
  • Find a set of video tutorials on time series analysis.
  • Watch the tutorials.
  • Take notes on the most important concepts.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Read the book Time Series Analysis by Forecasting by Robert Shumway and David Stoffer
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.
Show steps
  • 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
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
Show steps
  • Find a set of time series analysis problems.
  • Solve the problems.
  • Check your answers.
Join a study group or online forum
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
Show steps
  • Find a study group or online forum.
  • Participate in discussions.
Build a time series forecasting model for a real-world dataset
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
Show steps
  • 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
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

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Course image
Effort
9-12 hours/week
Level
Intermediate
Via
Coursera
Institution
Illinois Tech
Instructor
Trevor Leslie
Session
August 5, 2024 - October 14, 2024
Language
English
Good to know
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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