Business Forecasting Analytics
[ADM 4307 | FALL 2021]
Ahmet Kandakoglu
Virtual (Zoom)
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Office Hours Class Hours Program of study
Course Evaluation
Only by appointment.
Mondays 19:00-21:50
Mandatory course of option Healthcare Analytics and complementary option in Business Analytics.
In all e-mail communications, please include “ADM 4307” in the subject line, and your name and student number in the body of your message.
Class Location
Online (Zoom)
Prerequisite(s)
Optional course of option MISA/BTM.
Monday, November 29
COURSE DESCRIPTION
Forecasting Contest (Group work)
Nov. 29 10%
Course Deliverable
Weight on Final Grade
3 Assignments (Group work)
Oct. 14, Nov. 4, Nov. 11
30% (10% each)
Forecasting Project (Group work)
Final Exam
Final exam (administered online) date and time to be determined as per official exam schedule
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COURSE DESCRIPTION
Forecasting is an integral part of decision-making activities. Organizations define goals, seek to predict environmental factors, and then take actions that they hope will result in the achievement of these goals. Forecasting allows organizations to decrease their dependence on chance and become more scientific in dealing with their environments. Today, forecasting rests on solid theoretical foundations while also having a realistic, practical base that increases its relevance and usefulness to organizations.
This course covers the full range of major forecasting methods, provides a complete description of their essential characteristics, and presents the steps needed for their practical application, while avoiding getting bogged down in the theoretical details that are not essential to understanding how the various methods work. It also provides a systematic comparison of the advantages and disadvantages of the various methods so that the most appropriate method can be selected for each forecasting situation.
The course consists of lectures describing and discussing the relevant material. Learning will be enhanced by homework assignments and projects that will be handed in and marked, and will contribute, along with a group presentation, to your final course mark. Homework assignments and projects will consider several practical applications.
Students will also gain useful hands-on experience with the use of the R software environment. They will perform data pre-processing, visualize data using different plots, and apply various forecasting methods to analyze selected data sets to predict the future.
COURSE CONTRIBUTION TO PROGRAM LEARNING GOALS
This course contributes to the attainment of the B.Com. Learning Goals (LG) as follows:
LG2 Demonstrate Critical Thinking and Decision-making Skills
This course will focus on problem solving skills using forecasting methods to predict the future for decision making. Students will learn how to process, manipulate, and analyze data in today’s digital world, gain insights into the data, learn the sensible use of forecasting methods and the advantages and limitations of each method, and make informed decisions for businesses and organizations.
LG3 Demonstrate Leadership, Interpersonal and Communications Skills
Students will interact in a team environment. Assignments and projects will be done in groups helping students to develop their leadership, interpersonal, and communication skills.
LG7 Provide Value to the Business Community in a chosen Area of Specialization
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Business forecasting analytics is made possible through analysis of historical data using methods such as regression, ARIMA, and exponential smoothing, among others. Today’s businesses are very interested in forecasting analytics to gain insight into customer behavior and market trends.
COURSE LEARNING OBJECTIVES
Upon completion of this course, students will know the following subjects in detail:
o Forecasting in a business environment.
o Forecasting methodologies.
o Applications of forecasting and some practical issues.
o Timeseriesdecompositionandvisualization.
o Forecasting techniques including simple methods, exponential smoothing, ARIMA, and regression.
o JudgmentalforecastingmethodssuchasDelphimethodandscenarioforecasting.
o Introductiontoadvancedforecastingmethodssuchasdynamicregressionmodels,neuralnetworks,
and bootstrapping and bagging.
TEXTBOOK/COURSE PACKAGE
PowerPoint Slides
Additional References
Posted in advance of class on Brightspace
COURSE MATERIALS
WHERE TO GET IT
Hyndman, R.J., & Athanasopoulos, G. (2021) Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, Australia
https://otexts.com/fpp3
1. G. Box and G.M. Jenkins, Time Series Analysis: Forecasting and Control, Holden-Day, 1976.
2. S.A.Delurgio,ForecastingPrinciplesandApplications,Irwin-McGraw-Hill,1998.
3. J.E.HankeandD.W.Wichern,BusinessForecasting,9thed.Prentice-Hall,2009.
4. S. Makridakis, S.C. Wheelwright, and R.J. Hyndman, Forecasting Methods and Applications, 3rd ed., Wiley, 1998.
5. J.C. Nash and M.M. Nash, Practical Forecasting for Managers, and Oxford University Press, 2001.
6. T. Rey, A. Kordon, and C. Wells, Applied Data Mining for Forecasting Using SAS, SAS Press, 2012.
7. J.H.WilsonandB.Keating,BusinessForecasting,3rded.,Irwin-McGraw-Hill,1998.
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COURSE SCHEDULE
Note: This is a tentative weekly schedule. The schedule is subject to change and adjustment according to the class progress and other circumstances. An updated schedule will be posted on the course web site if necessary.
Lecture Topics
(Sept. 13)
3 o (Sept. 27) o
Time Series Decomposition Time Series Features
Assignment 1 posted
Forecasting project posted Assignment 2 posted
Forecasting contest posted Assignment 3 posted
Course Outline
o Introduction to Forecasting and R
Course Outline Chapter 1
Chapter 3, 4 Chapter 8
Chapter 9 Chapter 7 Chapter 7 Chapter 6
2 (Sept. 20)
Time Series Graphics The Forecaster’s Toolbox
o Simple Methods
o Transformations
o Residuals Diagnostics o Forecasting Accuracy o Forecast Package in R
Chapter 2, 5
4 (Oct. 04)
o Exponential Smoothing Models
5 (Oct. 11)
Thanksgiving
6 o (Oct. 18) o
8 o (Nov. 01)
9 o (Nov. 08) o 10 o (Nov. 15) o
Forecasting with ARIMA Models Solutions to Assignment 1
Forecasting with ARIMA Models
Regression Models for Forecasting Solutions to Assignment 2
Regression Models for Forecasting Solutions to Assignment 3
7 (Oct. 25)
Reading Week
11 (Nov. 22)
o Judgmental Forecasts
12 (Nov. 29)
Dynamic Regression Models Advanced Forecasting Methods
o Neural Networks
o Prophet Model
o Bootstrapping and Bagging
Some Practical Forecasting Issues
Chapter 10, 12, 13
Forecasting contest due
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Lecture Topics
13 (Dec. 06)
14 (Dec. 08)
o Project Presentations
o Project Presentations o Course Wrap-up
o Final Exam Preparation
Project reports due
INSTRUCTIONAL METHODS
This online course contains both synchronous and asynchronous activities, purposefully designed to provide flexibility in your learning process. The course is designed in a sequential module structure in Brightspace, with resources and complete assignment instructions to be provided for each topic and due dates noted. Synchronous activities will be completed during the scheduled online class sessions using Zoom, while asynchronous activities (such as assignments, project, and contest) can be completed online at any time once made available in Brightspace.
RECORDINGS OF SESSIONS
Class sessions may be recorded, and your image, voice and name may be disclosed to classmates. Note that by remaining in sessions that are being recorded, you are agreeing to the recording.
TECHNICAL REQUIREMENTS AND SUPPORT
The course requires that you to have a laptop or desktop computer with a reliable, high-speed Internet connection that allows you to watch videos, participate in discussion forums, upload images, and use your uOttawa Google Drive.
Video conferencing software (Zoom) is used for meeting with the instructor — so you will need to have a webcam and audio/voice capabilities through your computer. Zoom works on mobile/smart phones as well.
If you experience difficulties with Brightspace or with logins to any uOttawa systems, please do not contact the instructor or the course TA until you have tried to solve the problem through the IT supports in place at the University.
For all questions related to Brightspace, call the support line between 8 AM and 8 PM (Eastern) at 1-866-811-3201 OR submit an online request using this form 24 hours a day.
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For any other IT related issues, please contact IT services. They have a helpdesk that you can call, or you can submit a service ticket with a specific request 24 hours a day.
For problems connecting to the library services, you can also contact the Morisset Help Desk.
EXPECTATIONS FOR COMMUNICATIONS
I prefer email for communications. Please use my email for all communications related to the course.
Please ensure that you have set up your Brightspace account to receive notifications of announcements to your uOttawa email address — and please check your uOttawa email daily.
Likewise, I ask that you use your uOttawa.ca email address for sending messages. Make sure to include the course code, ADM 4307, in the subject line of your email.
If you have questions, please first contact the course TA, Abtahi, at
If you need further clarification, you can bring your questions to the lecture or schedule an appointment with me.
METHODS USED TO EVALUATE STUDENT PERFORMANCE
Team Building
Assignments, contest, and project will be done in teams of up to 3 students. Students are expected to find their team members within the first two weeks of the term and choose a team representative. The team representative will then enroll the team on the course website. Students without a team by the two-week deadline will be randomly assigned a team by the course instructor.
All team members are equally responsible for the deliverables and will receive the same mark for their teamwork.
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Student Course Performance Evaluation
Assignments (3 x 10%) 30% Forecasting contest 10% Forecasting project 20% Final Exam 40% Total 100%
Grades will be posted on the course website. If you believe that errors were made in the marking of your deliverable, please provide me with the original evaluation along with a short explanation of your concerns. The deadline for this is ten days after the date on which the grade of your evaluation was made available to you.
To pass the course students must achieve a passing grade of 50% on the Final exam and at least 50% out of the final course mark. Students who do not meet this requirement will receive a failing grade in the course.
Please note that it is not possible to submit extra course work to improve your mark.
Software Package
Students will be expected to use RStudio software package in class as well as for assignments and projects.
RStudio is a free and open-source integrated development environment for R, a programming language for statistical computing and graphics. It is capable of performing forecasting techniques. There will be tutorials on the use of R for forecasting purposes. Students are also required to consult on-line resources to learn more about R language and RStudio. More information on the product is available at https://www.rstudio.com and https://www.r-project.org.
Tutorial sessions covering the basics to get students started up with R will be offered by the course TA early during the term.
Homework Assignments
There will be a total of three (3) group assignments throughout the term. The assignments will help you review and practice the theory and methods you will learn in the lectures. The detailed format and requirements of each assignment will be communicated in class. Group assignments will be made available electronically through the course website.
Deliverable
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Whenever you present a forecast, please make sure that you clearly define the forecasting technique being used and all the variables that are part of your model. In addition, please provide a readable printout of the model and its solution whenever you use the computer. It may be a good idea to annotate this printout, so as to make it easier to understand. Use tables and figures when appropriate, in particular to present data and to interpret computer solutions in managerial terms. Tables and figures should be captioned and fully documented.
All assignments are to be submitted electronically as a single PDF file via Brightspace by the due date. Front page of the PDF document must include title of the assignment and names and student numbers of the members of the team submitting the assignment. The second page must be the Statement of Integrity signed by all members of the team.
Electronic submissions must be made before midnight (23:59) on the corresponding due date (see also the Weekly Course Schedule below). Submitted assignments must be neat, readable, and well-organized. Assignment marks will be adjusted for sloppiness, poor grammar and spelling, as well as for technical errors.
Assignments without all signatures on the Statement of Integrity will not be marked. The corresponding Personal Ethics Agreements documents are attached to this course outline. Students are asked to read the statement: “Beware of Academic Fraud” attached to this course outline and to consult and familiarize themselves with the University of Ottawa Academic Integrity website: http://web5.uottawa.ca/mcs-smc/academicintegrity/home.php.
Forecasting Contest
There will be a fourth group homework assignment in this course. This homework will be in the form of a forecasting contest and performed by the same teams as for the other assignments. Details about the specific forecasting situation will be discussed in class and posted on the course website. You must not work with any other students (besides your team partners) or obtain outside help. Please consult with the instructor if you need help or any clarification.
Contest outcomes are due on November 29th at the start of the class. Submit a softcopy of the report by the due date via Brightspace. Your forecasting contest mark will contribute 10% of your final course mark. The winner team (i.e., the best forecast) will receive a course mark bonus of 5%.
Forecasting Project and Group Presentation
The course also considers one (1) group project that will be performed by the same teams as for the other assignments. The project will help you review and practice the theory and methods you will learn in class through their application to a practical case study. The detailed format and requirements will be communicated in class. The group project will be made available electronically through the course website.
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You will be required to import the data into the R software environment, transform the data (if it is not in the required format), perform pre-processing tasks if necessary, and analyze the data by applying two or more forecasting techniques.
The project report is due on December 6th at the start of the class. Submit a softcopy of the report by the due date via Brightspace.
The project report should include:
• An executive summary (or abstract) (10 points)
• Explanation of the data set and the pre-processing tasks conducted to prepare the data (10
• Explanation of at least two forecasting techniques you performed on the data. Also, explain
why you considered these specific techniques for your dataset (30 points)
• Relevant graphs showing the output results of the techniques you applied (30 points)
• A conclusion section summarizing your findings, your understanding of the results, your
recommendation(s), and any useful patterns, prediction or future trends you might infer from
the data (10 points)
• Overall organization of the report, its soundness and readability, and quality of the
presentation (10 points)
Overall, your report should be 10 to 25 pages long (including graphs). Use 12 pt. Times New Roman font, with 1.5 or double space between the lines. Keep a margin of 1” on all sides of the page.
There will be a 20-minute group presentation on the project. Presentations will be performed by the same groups as the project. Your group report and presentation mark will contribute 20% of your final course mark equally.
Final Exam
The schedule of the final exam will be announced by the University. The final exam will be cumulative and cover all the material presented during the lectures and class discussions as well as lecture notes posted on the course website, assigned reading from the textbook, and other supporting materials distributed in class or posted on the course website. The exam will be administered online and will take about 3 hours.
Speed and accuracy are important – the exam usually requires some small calculations to see if students understand and are technically competent in basic forecasting methods.
Note: Deferred exams are managed and approved by the Student Services Centre, not by professors. The Student Services Center (SSC) is the only body that can approve and manage
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deferred final exams. Students must contact the SSC if they missed their final exam in order to complete the required form and submit their supporting documents (ex. medical certificate, family emergency, etc.).
EXPECTATIONS FOR STUDENT PARTICIPATION
Registered students are expected to attend all regularly scheduled classes for demonstration of important course material and to learn about the application of forecasting techniques in practice.
We will be using Zoom application to connect synchronously. As an essential aspect of academic integrity, do not share any of the details (i.e., link, sign-in information) with anyone outside this section of the course. If any issues with sharing such information arises (e.g., “zoombombing”, I will manage the issue, terminating our session if necessary. I hope not to have to do this, as these synchronous sessions are an essential part of building knowledge and skills in the course and help you prepare for the final exam.
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COURSE POLICIES
COURSE CONDUCT
The of Management prides itself on a strong sense of shared values drawing upon principles of respect, integrity, professionalism and inclusion to guide interactions inside and outside the classroom. The strives to provide a well-rounded and outstanding education enriched through experiential learning and a positive student experience.
PREVENTION OF SEXUAL VIOLENCE
The University of Ottawa is committed to a safe and healthy campus for work, for study and for campus community life for all members of the University community. The University, as well as various employee and student groups, offer a variety of services and resources to ensure that all uOttawa community members have access to confidential support and information, and to procedures for reporting an incident or filing a complaint. For more information, please visit uOttawa Sexual violence: support and prevention.
CLASS ATTENDANCE
Class attendance is expected and is necessary to successfully complete this course.
Students are expected to write (or submit) all cour
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