ADM 4307 – Fall 2021 Course Outline
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Business Forecasting Analytics
[ADM 4307 | FALL 2021]
Professor Ahmet Kandakoglu
Office Virtual (Zoom)
E-mail Ahmet.
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.
Office Hours Only by appointment.
Class Location Online (Zoom)
Class Hours Mondays 19:00-21:50
Prerequisite(s) ADM 2304
Program of study Mandatory course of option Healthcare Analytics and complementary option
in Business Analytics.
Optional course of option MISA/BTM.
Course Evaluation Monday, November 29
COURSE DESCRIPTION
Course Deliverable Due Date
Weight on
Final Grade
3 Assignments (Group work) Oct. 14, Nov. 4, Nov. 11
30%
(10% each)
Forecasting Contest (Group work) Nov. 29 10%
Forecasting Project (Group work) Dec. 06 20%
Final Exam
Final exam (administered online) date
and time to be determined as per official
exam schedule
40%
mailto:Ahmet.
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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 Time series decomposition and visualization.
o Forecasting techniques including simple methods, exponential smoothing, ARIMA, and regression.
o Judgmental forecasting methods such as Delphi method and scenario forecasting.
o Introduction to advanced forecasting methods such as dynamic regression models, neural networks,
and bootstrapping and bagging.
TEXTBOOK/COURSE PACKAGE
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
PowerPoint Slides
Posted in advance of class on
Brightspace
Additional References
1. G. Box and G.M. Jenkins, Time Series Analysis: Forecasting and Control, Holden-Day, 1976.
2. S.A. Delurgio, Forecasting Principles and Applications, Irwin-McGraw-Hill, 1998.
3. J.E. Hanke and D.W. Wichern, Business Forecasting, 9th ed. 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, Arnold Publishing 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. Wilson and B. Keating, Business Forecasting, 3rd ed., Irwin-McGraw-Hill, 1998.
https://otexts.com/fpp3
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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.
Week Lecture Topics Readings Note
1
(Sept. 13)
o Course Outline
o Introduction to Forecasting and R
Course Outline
Chapter 1
2
(Sept. 20)
o Time Series Graphics
o The Forecaster’s Toolbox
o Simple Methods
o Transformations
o Residuals Diagnostics
o Forecasting Accuracy
o Forecast Package in R
Chapter 2, 5
3
(Sept. 27)
o Time Series Decomposition
o Time Series Features
Chapter 3, 4 Assignment 1 posted
4
(Oct. 04)
o Exponential Smoothing Models Chapter 8
5
(Oct. 11)
Thanksgiving
6
(Oct. 18)
o Forecasting with ARIMA Models
o Solutions to Assignment 1
Chapter 9
Forecasting project posted
Assignment 2 posted
7
(Oct. 25)
Reading Week
8
(Nov. 01)
o Forecasting with ARIMA Models Chapter 9
Forecasting contest posted
Assignment 3 posted
9
(Nov. 08)
o Regression Models for Forecasting
o Solutions to Assignment 2
Chapter 7
10
(Nov. 15)
o Regression Models for Forecasting
o Solutions to Assignment 3
Chapter 7
11
(Nov. 22)
o Judgmental Forecasts Chapter 6
12
(Nov. 29)
o Dynamic Regression Models
o Advanced Forecasting Methods
o Neural Networks
o Prophet Model
o Bootstrapping and Bagging
o Some Practical Forecasting Issues
Chapter 10, 12, 13 Forecasting contest due
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Week Lecture Topics Readings Note
13
(Dec. 06)
o Project Presentations Project reports due
14
(Dec. 08)
o Project Presentations
o Course Wrap-up
o Final Exam Preparation
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.
https://tlss.uottawa.ca/site/support-form
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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 Ahmet. 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, Seyedeh Zahra 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.
https://it.uottawa.ca/
https://biblio.uottawa.ca/en/technical-support
mailto:Ahmet.
mailto:
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Student Course Performance Evaluation
Deliverable Value
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.
https://www.rstudio.com/
https://www.r-project.org/
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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.
http://web5.uottawa.ca/mcs-smc/academicintegrity/home.php
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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
points)
• 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 Telfer School 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 Telfer School strives to provide a well-rounded and outstanding
education enriched through experiential learning and a positive student experience. You are
encouraged to familiarize yourself with our expectations related to course conduct at the Telfer
School. Please refer to our Statement of Shared Rights and Responsibilities.
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 course deliverables as scheduled according to this
Course Outline. Absences for reasons listed in academic regulation 9.5 (with the appropriate
documented justification) are the only acceptable reasons for failure to hand-in or complete a
requirement of this course at the specified time. THERE WILL BE NO EXCEPTIONS. For a missed
mid-term or final examination, documentation must be submitted along with a deferred exam
application form to the Student Services Centre (DMS1100) of the Telfer School of Management.
Please visit the following webpage to download the form and carefully read the directives.
For other missed deliverables, the appropriate documentation can be submitted directly to the
Professor.
LANGUAGE & WRITING
You will be judged on your writing abilities on all written deliverables. It is recommended to take
the appropriate measures to avoid mistakes such as spelling, syntax, punctuation, inappropriate
use of terms, etc.
https://telfer.uottawa.ca/assets/bcom/documents/Statement-of-Shared-Rights-and-ResponsibilitiestV13.pdf
http://www.uottawa.ca/sexual-violence-support-and-prevention/
https://www.uottawa.ca/administration-and-governance/95-justification-absence-examination-or-late-submission-assignments
https://telfer.uottawa.ca/en/bcom/your-academic-world/exams/
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Professors show a marked bias for a movement from theory/definition (textbook, supplementary
readings, class discussion) to your own words to a concrete example. In other words, make a clear
reference to an accepted theoretical foundation, then explain it in your own words and then
provide a concrete example to support your idea (from a case study, from a class discussion, from
a real life situation that you have observed, from history,…).
In the event of poor language quality, you may be penalized up to 15% to the professor’s
discretion.
LATE SUBMISSIONS
Late submissions are not tolerated. Exceptions are made only for illness or other serious situations
deemed as such by the professor.
ABSENCES FROM EXAMS
University regulations require all absences from exams/quizzes and all late submissions due to
illness to be supported by a relevant documentation.
Absence for any reason must be justified in writing, to the Student Services Centre
( .ca) within five business days following the date of the exam.
Please visit the following webpage to download the deferral request form and carefully read the
directives. The Telfer School reserves the right to accept or refuse the reason.
Religious absences: If a religious holiday or a religious event will force you to be absent during an
evaluation, it is your responsibility to inform your professor and the Student Services Centre as
early as possible.
INTELLECTUAL PROPERTY
All forms (printed, digital, etc.) of course materials prepared by the instructor (including e-mailed
or Brightspace content) are protected by copyright. This covers all files, assessments, solutions,
cases, and other materials. Copying, scanning, photographing, posting, or sharing by any means is
a violation of copyright and will be subject to appropriate penalty as prescribed by University of
Ottawa regulation.
ACADEMIC INTEGRITY
Academic Regulation 14 defines academic fraud as “any act by a student that may result in a
distorted academic evaluation for that student or another student. Academic fraud includes but is
not limited to activities such as:
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a) Plagiarism or cheating in any way;
b) Submitting work not partially or fully the student’s own, excluding properly cited
quotations and references. Such work includes assignments, essays, tests, exams,
research reports and theses, regardless of whether the work is written, oral or another
form;
c) Presenting research data that are forged, falsified or fabricated;
d) Attributing a statement of fact or reference to a fabricated source;
e) Submitting the same work or a large part of the same piece of work in more than one
course, or a thesis or any other piece of work submitted elsewhere without the prior
approval of the appropriate professors or academic units;
f) Falsifying or misrepresenting an academic evaluation, using a forged or altered
supporting document or facilitating the use of such a document;
g) Taking any action aimed at falsifying an academic evaluation.”1
The Telfer School of Management does not tolerate academic fraud. Please familiarize yourself
with this guidance.
http://web5.uottawa.ca/mcs-smc/academicintegrity/home.php
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STUDENT SUPPORT SERVICES
Academic GPS
The Academic GPS hub is a one-stop shop for academic support. Whether you’re an experienced
student or just starting out, you’ll find some great resources to help you succeed.
With the Academic GPS, you can:
• chat with a mentor seven days a week
• register for study groups
• take part in study methods workshops (note taking, time management, exam
preparation, stress management, etc.)
• book an appointment with a mentor
For more information: uOttawa.saea-tlss.ca/en/academic-gps
Health and Wellness
Your wellness is an integral part of your success. If you don’t feel well, it can be hard to focus on
your studies. Dedicated professionals and fellow students who care about you are always ready
to provide advice and support. Depending on your needs, many activities and services exist to
accompany you during your academic journey. Services include:
• opportunities to connect;
• counselling sessions
• peer support;
• physical activity;
• wellness activities and workshops;
• spiritual guidance.
If you want to connect with a counsellor, you can book an appointment online or go to their
walk-in clinic at 100 Marie-Curie, fourth floor. You can also drop-in to our wellness space, chat
online with a peer helper, or access 24/7 professional help through the website.
For more information and to access these services, go to uOttawa.ca/wellness.
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https://www.uottawa.ca/wellness/
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Academic accommodations
We try to make sure all students with disabilities have equal access to learning and research
environments, the physical campus and University-related programs and activities. The
Academic Accommodations service works with other campus services to create an accessible
campus learning environment, where students with disabilities have an equal opportunity to
flourish. We offer a wide range of services and resources, provided with expertise,
professionalism and confidentiality.
Some services we offer
• Help for students with disabilities in making the transition
• Permanent and temporary accommodation measures
• Learning strategy development
• Adaptive exams
• Transcriptions of learning material
• Interpretation (ASL and LSQ)
• Assistive technologies
If you think that you might need any of our services or supports, email the Academic
Accommodations service ( ).
OTHER U OTTAWA SERVICES THAT YOU MIGHT FIND USEFUL
• Career Services:
o Telfer Career Centre
o U Ottawa Career Services
• Counselling Service
mailto:
mailto:
mailto:
http://www.telfer.uottawa.ca/careercentre/en
http://www.sass.uottawa.ca/careers
http://www.sass.uottawa.ca/personal/
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PERSONAL ETHICS STATEMENT CONCERNING TELFER SCHOOL
ASSIGNMENTS
Group Assignment:
By signing this Statement, I am attesting to the fact that I have reviewed not only my own work, but the
work of my colleagues, in its entirety.
I attest to the fact that my own work in this project meets all of the rules of quotation and referencing in
use at the Telfer School of Management at the University of Ottawa, as well as adheres to the fraud policies
as outlined in the Academic Regulations in the University’s Undergraduate Studies Calendar Academic
Fraud Webpage.
To the best of my knowledge, I also believe that each of my group colleagues has also met the rules of
quotation and referencing in this Statement.
I understand that if my group assignment is submitted without a signed copy of this Personal Ethics
Statement from each group member, it will be interpreted by the Telfer School that the missing student(s)
signature is confirmation of non-participation of the aforementioned student(s) in the required work.
______________________________ __________________________
Signature Date
______________________________ __________________________
Last Name (print), First Name (print) Student Number
______________________________ __________________________
Signature Date
______________________________ __________________________
Last Name (print), First Name (print) Student Number
______________________________ __________________________
Signature Date
______________________________ __________________________
Last Name (print), First Name (print) Student Number
https://www.uottawa.ca/vice-president-academic/academic-integrity
https://www.uottawa.ca/vice-president-academic/academic-integrity
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Personal Ethics Statement
Individual Assignment:
By signing this Statement, I am attesting to the fact that I have reviewed the entirety of my attached work
and that I have applied all the appropriate rules of quotation and referencing in use at the Telfer School of
Management at the University of Ottawa, as well as adhered to the fraud policies outlined in the Academic
Regulations in the University’s Undergraduate Studies Calendar Academic Fraud Webpage.
______________________________ __________________________
Signature Date
______________________________ __________________________
Last Name (print), First Name (print) Student Number
https://www.uottawa.ca/vice-president-academic/academic-integrity