Illinois Tech
Introduction to Time Series
Illinois Tech

Introduction to Time Series

Trevor Leslie

Instructor: Trevor Leslie

Included with Coursera Plus

Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

51 hours to complete
3 weeks at 17 hours a week
Flexible schedule
Learn at your own pace
Build toward a degree
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

51 hours to complete
3 weeks at 17 hours a week
Flexible schedule
Learn at your own pace
Build toward a degree

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

27 assignments

Taught in English

See how employees at top companies are mastering in-demand skills

Placeholder
Placeholder

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

Placeholder

There are 9 modules in this course

Welcome to Introduction to Time Series! In this module we'll define time series and time series models, and we'll develop some intuition for the fundamental concept of stationarity, and why it's useful.

What's included

8 videos6 readings4 assignments1 discussion prompt

In this module, we'll discuss stationarity in more detail. We'll learn the technical definitions of weak and strong stationarity, and explain why the weaker version is more practical to use. We'll discuss the autocovariance and autocorrelation functions for stationary processes---concepts that will be with us for the rest of the course. And finally, we'll see some examples of ARMA processes, which we'll treat more deeply in the coming modules.

What's included

9 videos3 readings3 assignments

In this module, we'll focus on ARMA processes, and what is arguably their most important feature, namely their autocorrelation structure. We'll see how to compute these "from scratch" (with a little help from R for the computations), and look at plots of the autocorrelation function (ACF) to get some intuition for how the ACF of an ARMA process behaves and what it can tell us.

What's included

10 videos4 readings3 assignments

In this module, we begin by discussing the ACF's of more complicated ARMA processes. Our main focus, though, is on one-step-ahead forecasts. We learn about the best linear predictor: both how it is defined and how to use it. Finally, we use what we have learned in order to define the Partial Autocorrelation Function (PACF), which is another fundamental tool in the study of stationary processes.

What's included

9 videos3 readings3 assignments

In this module, we learn about fitting a stationary time series model to data. The fitting process involves determining what values of the parameters to use. We discuss preliminary estimation and maximum likelihood estimation of these parameters.

What's included

9 videos4 readings4 assignments

In this module, we discuss model diagnostics and order selection. Given an ARMA order, we've already seen how to best fit the parameters of the associated model. Given several different fitted models, the tools we develop in this module will allow us to make an intelligent choice about which one to use.

What's included

7 videos3 readings3 assignments

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. The second lesson extends these skills to SARIMA models, focusing on identifying seasonality and fitting these models to capture seasonal patterns in data.

What's included

9 videos3 readings3 assignments

This module equips students with more sophisticated forecasting techniques beyond one-step-ahead predictions. We treat both (S)ARIMA models and exponential smoothing models and show how to handle forecasts in R. For the simplest of these models, we look inside the "black box" a little bit and demonstrate how these forecasts are generated.

What's included

9 videos3 readings3 assignments

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.

What's included

1 assignment

Instructor

Trevor Leslie
Illinois Tech
1 Course654 learners

Offered by

Illinois Tech

Recommended if you're interested in Data Analysis

Build toward a degree

This course is part of the following degree program(s) offered by Illinois Tech. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹

Why people choose Coursera for their career

Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

New to Data Analysis? Start here.

Placeholder

Open new doors with Coursera Plus

Unlimited access to 7,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Frequently asked questions