LOGM 630: Forecasting Management

Companies, governments and other organizations now collect and analyze huge amounts of data about suppliers, clients, employees, citizens, transactions, and much more. There are a number of ways organizations can use this data. Business analytics uses this data to make better decisions and forecasting is a powerful and commonly used branch of business analytics. Forecasting especially can provide a potent toolkit for analyzing time series data.

This course focuses on forecasting time series, where past and present values are used to forecast future values of a series of interest. This course covers issues relating to different steps of the forecasting process, from goal definition, through data visualization, modeling, and performance evaluation to model deployment.

Class Information

Course Objectives

The primary objective of this course is to help the student understand the basic analytic techniques and processes for forecasting and how to use them to make better decisions. More specifically, at the completion of the course, each student should be able to:

  • Define a forecasting task and workflow
  • Evaluate a forecasting performance
  • Apply and be familiar with popular forecasting methods
  • Explore, identify and model different types of patterns in time series
  • Be able to implement a forecasting process in practice


Students can use one of the following books. Both books cover the same topics, one focuses on implementing the techniques in the R programming language while the other focuses on implemention with XLMiner, an Excel add-on.

  • Practical Time Series Forecasting with R: A Hands-On Guide (2nd Ed.) by Shmueli & Lichtendahl, ISBN-10: 0997847913
  • Practical Time Series Forecasting: A Hands-On Guide (3rd Ed.) by G. Shmueli, ISBN-10: 0991576659

In addition to the book, students will participate in a 6 week free online course (Business Analytics Using Forecasting) led by the author of the textbook. This course closely aligns with the book and covers the first 7 chapters. Students will need to register for this couse and be prepared to start the first session on Oct 2nd. Students can find a link to register for this course at http://www.forecastingbook.com/mooc.

Students can also leverage the textbook’s website (http://www.forecastingbook.com/home) for additional resources such as data, R code, XLMiner tutorial videos, and additional video discussions for each chapter of the book.

Class Structure

This course blends textbook reading with online lectures and demonstrations that emphasize discussion and illustration of methods, as well as hands-on, practical applications that provide both a sound base of learning and an opportunity to test and develop skill. Students will blend learning from the textbook with the 6 week online course. A flipped classroom will be emphasized where students spend time outside the classroom learning the material via the textbook and online course and the majority of in-class activities will be reserved to review, clarify, and do hands-on projects. Thus, students should bring a laptop to class and be prepared to implement the tools and skills they are learning. Students should expect to dedicate approximately 2 hours of time outside of the classroom performing coursework for every 1 hour in the classroom.

Performance Evaluation

This is a project-based course. Depending on enrollment, students will work in groups of 4-5 students. Throughout the course each group will spend classroom time completing all three of the case studies listed in the back of the textbook. Your final course grade will be determined according to the following requirements and their respective weights.

Final grades will be distributed according to the following cutoffs:

  • A     94 – 100%
  • A-    90 – 93%
  • B+    87 – 89%
  • B      83 – 86%
  • B-    80 – 82%
  • C+    77 – 79%
  • C      73 – 76%
  • C-    70 – 72%
  • D & F   Hopefully None!

Case Study Evaluations

Throughout the term you will progressively complete all three case studies. You will submit your case study progress two times during the term for progress evaluations. These progress checks will provide you direction for final completion. Grading rubric.

Group Presentation

During week 10, each group will select one of their case studies to present. Although your case study analysis may only be 90% complete at this time, this provides an opportunity to present your work to date and receive feedback from the class and instructor on where improvements and clarification could be made. Further guidance regarding expectations for the group presentation will be provided in class. Presentation schedule.

Final Case Study

Throughout the term you will progressively complete all three case studies and the instructor will randomly draw one of the group’s three submissions to grade for their final project. Grading rubric.


At the end of the term I will have all students perform a peer assessment of their small group members. This assessment will rate each member in several areas regarding:

  • Engaging in quality discussions with the group to improve forecasting knowledge
  • Coming to each class prepared to further the case study analyses
  • Providing adequate contribution to group work
  • Working well as a team member in the small group activities
  • etc.

I will use this feedback to help in determining your level of engagement.



Week Dates Lesson Description Learning Material Deliverables Class Material
1 Oct 2-6 Forecasting approach & data Ch 1-2 MOOC week 1    
2 Oct 9-13 Performance Evaluation Ch 3 MOOC week 2-3  
3 Oct 16-20 Overview of forecasting methods & Understanding smoothing methods Ch 4-5 MOOC week 4  
4 Oct 23-27 Understanding smoothing methods Ch 5 MOOC week 4 Case studies Eval #1
5 Oct 30-Nov 3 Case study review & wingman day NA    
6 Nov 6-10 Regression models: trend, seasonality, & AR Ch 6-7 MOOC week 5-6  
7 Nov 13-17 AR & MA Models Ch 7 Case studies Eval #2
8 Nov 20-24 ARIMA Models Ch 7    
9 Nov 27-Dec 1 Forecasting binary outcomes & Group presentations Ch 8  
10 Dec 4-8 Group presentations   Group presentation  
11 Dec 11-15 Finals week   Case studies due  


This is an applied course and you will be using software during class time. Therefore, plan on bringing a computer to each class meeting. Furthermore, you will need some software to perform the forecasting procedures.

  • R and RStudio: Students familiar with the R programming language can leverage this software. You can find details on how to download these here. This is the software the instructor uses and is familiar with.
  • XLMiner: Students that are not comfortable with R programming can use the free student version of XLMiner. Note that the instructor is not familiar with this software so you will be on your own for troubleshooting. You can find details on how to download this sofware here.
  • Slack will replace e-mail and Blackboard for our course. You will receive an invitation to the AFIT Forecasting slack team. You may wish to install one of the apps.


  1. Attendance: Attendance at all class sessions and exams is mandatory for military and civilians assigned to AFIT as full-time students except for extenuating circumstances. Scheduled classes and exams are defined by the instructor and they are documented in the course schedule. Part-time students are expected to attend scheduled classes, and absences should be explained to the instructor. The student should provide advance notice, if possible. (References: Student Handbook, Graduate School Catalog)
  2. Academic Integrity: All students must adhere to the highest standards of academic integrity. Students are prohibited from engaging in plagiarism, cheating, misrepresentation, or any other act constituting a lack of academic integrity. Failure on the part of any individual to practice academic integrity is not condoned and will not be tolerated. Individuals who violate this policy are subject to adverse administrative action including disenrollment from school and disciplinary action. Individuals subject to the Uniform Code of Military Justice may be prosecuted under it. Violations by government civilian employees may result in administrative disciplinary action without regard to otherwise applicable criminal or civil sanctions for violations of related laws. (References: Student Handbook, ENOI 36 – 107, Academic Integrity)
  3. Academic Grievance: AFIT and the Graduate School of Engineering and Management affirm the right of each student to resolve grievances with the Institution. Students are guaranteed the right of fair hearing and appeal in all matters of judgment of academic performance. Procedures are detailed in ENOI 36 – 138, Student Academic Performance Appeals.
  4. Testing Policy: This is a project-based course. Consequently there will be no midterm or final exam.
  5. Late Assignments and Make-Ups: Late submissions will not be accepted.
  6. Tentative Plan: The course syllabus is a general plan for the course; deviations announced to the class by the instructor may be necessary.