Welcome to Forecasting and Risk Analysis (BANA 4090)! 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 the 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, Holt-Winters Model, ARMA, ARIMA models and model performance assessments. Implementation issues and challenges are discussed.
Class Information
- Name: Jiantong Wang
- Title: PhD Candidate, Department of Operations, Business Analytics and Information Systems
- Office Information: LCB, Room 3327
- Email: wang5jt@mail.uc.edu
- 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 Canvas. 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 and code demonstrating the relevant methods.
| Module | Description |
|---|---|
| 1 | Module 1 – Introduction to Time Series Analysis (weeks 1-3) |
| R lab-1 | • Introduction to R & Rstudio |
| R lab-2 | • Visualization for Time Series Data |
| R lab-3 | • Wrangling with Time Series Data |
| Review Session - 1 | • Review for Module 1 |
| Mini Cases Studies - 1 | • Mini Case Studies for Module 1 |
| 2 | Module 2 – Forecasting Models: State Space Models (weeks 4-7) |
| R lab-4 | • R lab (Basic Tools for Forecasting-I) |
| R lab-5 | • R lab (Basic Tools for Forecasting-II) |
| R lab-6 | • R lab (Evaluation of Model Performance) |
| Review Session - 2 | • Review for Module 2 |
| Mini Cases Studies - 2 | • Mini Case Studies for Module 2 |
| Midterm Exam | Midterm Exam (weeks 8) |
| Practice Exam | • Solution and Explanation to Practice Exam |
| 3 | Module 3 – Forecasting Models: ARIMA (weeks 9-11) |
| R lab-7 | • R lab (ARIMA-I: ACF and PACF) |
| R lab-8 | • R lab (ARIMA-II: ARMA and ARIMA) |
| R lab-9 | • R lab (Seasonal ARIMA models) |
| Review Session - 3 | • Review for Module 3 |
| Mini Cases Studies - 3 | • Mini Case Studies for Module 3 |
| 4 | Module 4 – Final Project (weeks 12-13) |
| Final Project Guidelines | • Guidelines to Final Project |
| Possible Datasets | • Provided Possible Datasets for Final Project |
| Possible Datasets Description | • Description to Provided Possible Datasets |
| Example Report | • Example Final Project Report |
Description of Major Assignments
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Exams – There will be one exam given during class after we finish module 2 to assess grasp of key concepts of time series analysis and forecasting.
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Assignments – 3 take-home assignments will be given throughout the semester. Students will have 2 weeks to complete the assignments.
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Weekly Practice Quizzes – For each week, we’ll have a practice quiz without time limit. The aim of setting practice quizzes is to help you solidify your understanding of the concepts we cover in class and build your confidence in applying what you learn.
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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 9 and the analyses will be presented during week 13. 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:
- Rob J Hyndman and George Athanasopoulos, Forecasting: Principles and Practice (3rd ed)
- Bradley Boehmke, UC BANA 6043 Statistical Computing
- Ethan Swan, Python for Data Science
- And many more.