Week-1 Introduction to Course
Some of the slides are adapted from the lecture notes provided by Prof. Antoine Saure and Prof. Rob Hyndman
Business Forecasting Analytics
ADM 4307 – Fall 2021
Introduction to Course
Ahmet Kandakoglu, PhD
13 September, 2021
Contact Details
• Lecturer
• Ahmet Kandakoglu, PhD
• Office: Room 7116, Desmarais Building
• E-mail: Ahmet.
• Office Hours: Only by appointment (Virtual)
• Teaching Assistant
• Seyedeh Zahra Abtahi
• E-mail:
• 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
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Brief Bio
• Education
• BSc in Computer Engineering
• MSc and PhD in Industrial Engineering (Operations Research)
• Postdoc in Operations Research
• Senior Lead Data Scientist (SITA)
• Part-Time Professor (UOttawa, McGill, Carleton)
• Business Intelligence Tech. & Big Data Analytics
• Business Data Mining
• Business Forecasting Analytics
• Operations Management
• Information Systems
• Applications of Statistical Methods in Business
• 15+ years of experience in various advanced analytics positions
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Course Learning Objectives
• To obtain an understanding of common statistical methods used in business
and economic forecasting
• To develop the computer skills required to forecast business and economic
time series data
• To gain insights into the problems of implementing and operating large scale
forecasting systems for use in business
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Course Deliverables and Assessment
• Three assignments,
• One project and one contest,
• Final exam (3 hours duration)
• Need at least 50% for final exam and 50% for total.
Course Deliverable Due Date
Weight on
Final Grade
3 Assignments
(group work)
Oct. 04, Oct. 18, Nov. 01
30%
(10% each)
Forecasting Project
(group work)
Nov. 22 20%
Forecasting Contest
(group work)
Dec. 06 10%
Final Exam Official final exam period 40%
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Teaching Approach
• Lectures:
• Online (Zoom)
• Mondays 19:00 – 21:50
• Lectures notes, recordings and additional course materials will be posted
on Brightspace course website
• Office hours:
• Virtual (Zoom)
• Only by appointment
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Brightspace Course Website
• Includes all lecture notes, handouts, datasets, assignments, project and practical
questions.
• Discussion forum for asking questions
• Lecture recordings and Zoom meeting links
• Assignment and project submissions
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Weekly Schedule
Week Topic Chapter
1
Course Outline
Introduction to Forecasting and R
Chapter 1
2
Time Series Graphics
The Forecaster’s Toolbox
Chapter 2, 5
3
Time Series Decomposition
Time Series Features
Chapter 3, 4
4 Exponential Smoothing Models Chapter 8
6-8 Forecasting with ARIMA Models Chapter 9
9-10 Regression Models for Forecasting Chapter 7
11 Judgmental Forecasts Chapter 6
12
Dynamic Regression Models
Advanced Forecasting Methods
Some Practical Forecasting Issues
Chapter 10, 12, 13
13-14 Project Presentations –
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Textbook
• Hyndman, R.J., & Athanasopoulos, G. (2021) Forecasting: Principles and
Practice, 3rd edition. (https://otexts.com/fpp3).
• Free and online
• Data sets in associated R packages
• R code for examples
• Print version of the book is available from Amazon
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Software Package
• Students will be expected to use RStudio software in class as well as for
assignments and projects.
• Install required packages
• install.packages(c(“tidyverse”, “fpp3”))
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Recommended Courses on DataCamp & Coursera
All courses are free and highly recommended.
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What Do I Expect From You?
• Keep on top of the material
• Attendance
• Read the textbook
• Office hours – don’t wait till the project due date and final exam
• Ask questions!
• I can’t help you if I don’t know you are struggling
• Familiarize yourself with R Studio
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A Note on Academic Fraud
• Sharing completed or even partially completed assignments constitutes fraud
• You can work together on assignments but everyone must hand in their own work
• Talk, work together but do not share documents that you have worked on with anyone
• Having an electronic device (i.e., cell phone) on your person during an exam is
fraud
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Before we start…
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