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This course is for junior/senior undergraduate students. The main objective of this course is to provide you with the proper foundation to analyze and forecast time series data in a professional setting. This means that in addition to forecasting into the future and evaluating such forecasts we will discuss other topics to prepare you for your journey as an analyst. A survey of analytical techniques used in forecasting. Techniques include exponential smoothing, ARIMA, multiple regression, and judgement-based methods. Implementation issues and challenges are discussed.

Class Information

  • Name: Zhaohu (Jonathan) Fan
  • Title: PhD Candidate, Department of Operations, Business Analytics and Information Systems
  • Office Information: LCB, Room 3327
  • Email: [email protected]
  • Office Hours: Monday & Wednesday 11:00 AM to 12:00 PM and by appointment

Communication Policy: Students are encouraged to contact me anytime via email or phone. Please use email as the primary mode of contact. A response will be given within 36-48 hours. Please understand that I cannot guarantee an immediate response if you contact me very close to an assignment deadline.

Learning Objectives

  • Provide students with a foundational knowledge of time series analysis
  • Expose students to a number of traditional and contemporary methods in time series and forecasting
  • Familiarize students with many of the challenges associated with time series forecasting
  • Provide students with practical experience analyzing real-world data and communicating the results

While I will try to focus on the application over the theory to maximize the above objectives, I will provide additional optional reading for those interested in a deeper dive into the theory 🚀.

Upon successfully completing this course, you will be able to:

  • have a solid foundation for approaching time series analysis and forecasting projects in the future
  • provide of project for portfolio
  • practice with R programming language as it relates to time series data

Lecture materials (slides) and code demonstrating the relevant methods.

Module Description
1 Module 1 – Practical Time Series Analysis (weeks 1-3)
Welcome Lecture and Notes • Why is time series analysis and forecasting important?
Practical Time Series Analysis • How to approach a forecasting problem
R lab-I • Visualization and exploration of time series data
R lab-II • Wrangling with time series objects
R lab-III • Features of time series data
2 Module 2 – Forecasting Basics (weeks 4-6)
Forecasting Basics-Part I •Four simple forecasting models
Forecasting Basics-Part II • Forecasting process
R lab-IV • R lab (Basic tools For forecasting-I)
R lab-VI • R lab (Basic tools For forecasting-II)
R lab-VII • R lab (Evaluation of model performance-I)
3 Module 3 – Forecasting Models (weeks 7-10)
ARIMA-Part I • ARIMA models-I
ARIMA-Part II • ARIMA models-II
ARIMA-Part III • ARIMA models-III
ARIMA-Part IV • Seasonal ARIMA models
R lab-VIII • R lab (Evaluation of model performance-II)
R lab-IX • R lab (ARIMA-I)
R lab-X • R lab (ARIMA-II)
4 Module 4 – Additional Topics (weeks 11-14)
Additional Topics • Exponential smoothing-I
R lab-XI • R lab (Simple Exponential Smoothing)
R lab-XII • R lab (Holt's Method)
R lab-XIII • R lab (Holt-Winter Method)

Description of Major Assignments

  • Exams – There will be two exams given during class after we finish module 2 and module 4 to assess grasp of key concepts of time series analysis and forecasting.

  • Assignments – 4 take-home assignments will be given throughout the semester. Students will have 2 weeks to complete the assignments.

  • Discussions – The first 14 classes will have in-class labs/discussions. Write-ups from these discussions/labs are to be submitted on Canvas by 11:59pm on the Sunday after class. If you cannot make the class, these will be posted and submission will be allowed.

  • Project – The project is a core component of this course. Students will pick a topic and dataset of their choosing to analyze and forecast with the methods taught in this course (and ideally some additional methods). These topics will be submitted during week 6 and the analyses will be presented during week 15. In lieu of a final exam, students will submit a reproducible markdown/notebook.

Class video, homework and class projects are available in Canvas. Please check homework in Canvas and submit all your homework through Canvas. All announcements are in Canvas.

Acknowledgments: I have drawn ideas or readings from the following texts: