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Syllabus |
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1) Textbook Access: Barry Keating and J. Holton Wilson. Forecasting and Predictive Analytics, Seventh Edition. The book will be accessed through your CONNECT account from McGraw-Hill. Note:
Do NOT purchase the hardcopy of the text! This class will use McGraw-Hill's CONNECT (LearnSmart) learning software to access an enhanced digital version of the textbook and accompanying materials.
2) ForecastX For Excel (statistical software downloaded with instructions emailed to you and also available in the Mendoza labs); this software is compatible with Windows 7, Windows 10 and Office 2016. This software will be made available for download to registered students. This software is only available for Windows. While not available for Macintosh computers, this software is available on all Mendoza College machines.
3) XLMiner (not available on any college machines or servers). This software is available for download to registered students for a fee. This software is only available for Windows (but may be accessed through a browser for Macintosh users)..
4) Prevedere (accessed through a browser). The Prevedere access will be available directly to registered students.
Other software we will use is available on College servers: IBM/SPSS Modeler and SAS Enterprise Miner; these commercial packages are not available for student download.
Apps are available for IOS and Android for LearnSmart, SmartBook, and ReadAnywhere. These Apps will allow you to access course materials from any location using a variety of devices. After downloading the Apps, link them to your CONNECT account and this class section.
"I believe that forecasting or demand management may have the potential to add more value to a business than any single activity within the supply chain. I say this because if you can get the forecast right, you have the potential to get everything else in the supply chain right. But if you can't get the forecast right, then everything else you do essentially will be reactive, as opposed to proactive planning."
Al Enns, Director of Supply Chain Strategies, Motts North America, Stamford, Connecticut
We might think of business prediction as having developed in three stages. One way of looking at this development is illustrated in the figure below. Almost anything you think about has gone through some evolution. In lighting, we went from primitive torches to kerosene to the first light bulbs developed by Edison in the late 1870s. Incandescent light bulbs like the one at the left in the figuure became a worldwide standard for many years. Such light bulbs had a relatively short life, were hot, and were not energy efficient. In the late 20th century, new, more energy-efficient, longer lasting, but still hot compact flourescent bulbs became common. Then, in the early 21st century, LED bulbs that were cool, energy efficient, and long-lasting became the new norm.
Forecasting has gone through an evolution as well. The oldest of the predictive models, time series models, are covered at the beginning of the course (Stage I). The second stage of the course moves into causal demand planning models (Stage II). But the emergence of "Big Data" has brought with it an increased need for additional analytical tools; these analytics tools will comprise the last half of the course. Today every organization is confronted with a deluge of data. Organizations have more data than can easily be converted into actionable information using traditional statistical methods. This is "Stage III" in the evolution of forecasting and predictive analytics.
The single goal of this course is to enable you to “turn data into information." Data is simply numbers, text, audio, and graphics; data is just facts and figures. Information, however, is data that is processed, organized, structured or presented in context so as to make it useful for informing decisions.
There is a certification process available to forecasters much like the Certified Financial Analyst designation or the Certified Public Accountant designation. The Certified Professional Forecaster designation is available through the Institute of Business Forecasting.
Course Learning Objectives:
If you stay engaged in the course and complete the suggested readings in SmartBook and LearnSmart and the assignments in CONNECT:
Attendance:
Regular attendance is essential to the successful completion of this course. Attendance will regularly be taken and you are responsible for material covered in class whether or not you have attended class. Missing more than four class sessions unexcused will result in an automatic reduction in course grade. You should plan on spending at least two hours of independent study in CONNECT for each hour of class attendance.
Grading:
A course grade will be assigned on the basis of student performance on two midterm examinations, a final examination, and assignments. The assignments and textbook problem solutions will sometimes be presented in class by students.
Quizzes, Assignments, and Problems (completed in CONNECT): thirty percent of the course grade
First Midterm Exam : twenty percent of the course grade
Second Midterm Exam : twenty percent of the course grade
Final (comprehensive) Exam : thirty percent of the course grade
Gradebook available in CONNECT.
Quizzes, Assignments and Problems:
On the attached "assignment sheet" you will find approximate class-by-class list of topics to be covered and your reading assignment. Exact due dates and times will appear with each assignment in CONNECT.LearnSmart assignments in the CONNECT textbook are to be completed on the due date and time. Exercise/problem assignments are assigned and handed in through CONNECT. Late assignments of any type are not accepted.
Assignments (essentially longer problems, directed exercises, or reviews of articles presented in class) will be assigned for some of the topics covered and will be discussed by students in class. The class presentation of assignments and textbook problems (using your computer) is an important and integral part of the course.
Quizzes take place at the beginning of each class and are administered in CONNECT. There are no makeups for quizzes.
Note that all the data sets for the figures, tables, and exercises in the textbook are available in CONNECT (often in Excel format). There is no need for you to "key in" any of the data sets used in the course.
In CONNECT there are short videos that detail how to implement the various procedures we will use in software.
Midterm Examinations:
The examinations will be full-period examinations of essentially a problem-solving nature; problems and questions will be similar to those asked of you in CONNECT. Because of the technical nature of the examination, students are allowed to use calculators (not a smartphone calculator!). The examination, however, is to be completed without reference to the textbook, class notes or any other materials. Grades for these examinations will be available in CONNECT. Completed examinations will be returned to students after grading.
Honor Code:
The University of Notre Dame Academic Code of Honor is observed in this class. Violation of the Code of Honor consists of misrepresenting, in any way, anyone else’s work as your own, any verbal or written misrepresentations to the instructor, any use of unauthorized external materials during quizzes and/or tests, or any collaborative effort on the examinations. All members of the class have an equal and shared responsibility to enforce the code of ethics among their peers.
Final Comprehensive Examination:
A comprehensive final examination will be administered during the "final examination period" of the university at the Registrar's selected time and date.
The Project:
Note: This semester undergraduate students will not have a semester-long project!
Missing Assignments:
Assignments or problems should be handed in through CONNECT on the due date (or when the instructor indicates, as adjustments are made through the semester). Assignments not handed in on the due date will receive a grade of zero. Some problems and exercises will be presented in class by students. Handing in a solution on CONNECT does not substitute for a presentation and explanation when called upon.
Class# /Date /Topic Assignment
This course meets every Tuesday and Thursday.
1 8/27 (Tuesday) Introduction to Business Forecasting, -- Chapter 1 Introduction to Forecasting and Predictive Analytics
- Overview of the ForecastXTM computing package
- Overview of the XLMinerTM computing package
- Overview of PrevedereTM
- Using CONNECT (for reading, studying, practice quizzes and exams, assignments, and grading)
2 8/29 (Thursday) Continued -- Chapter 1 Introduction to Forecasting and Predictive Analytics
3 9/3 (Tuesday) Chapter 2 -- The Forecast Process, Data Considerations, and Model Selection
4 9/5 (Thursday) Chapter 2 -- The Forecast Process, Data Considerations, and Model Selection
5 9/10 (Tuesday) Chapter 3 -- Extrapolation 1. Moving Averages and Exponential Smoothing
6 9/12 (Thursday) Moving Averages and Exponential Smoothing -- Chapter 3 -- Extrapolation 1. Moving Averages and Exponential Smoothing
7 9/17 (Tuesday) Moving Averages and Exponential SmoothiExtrapolation 2. Introduction to Forecasting with Regression Trend Models
8 9/19 (Thursday) Chapter 4 -- Extrapolation 2. Introduction to Forecasting with Regression Trend Models
9 9/24 (Tuesday) Chapter 4 (continued) -- Extrapolation 2. Introduction to Forecasting with Regression Trend Models
10 9/26 (Thursday) Chapter 4 (continued) -- Extrapolation 2. Introduction to Forecasting with Regression Trend Models
11 10/1 (Tuesday) First Midterm Examination
1210/3 (Thursday) Chapter 5 --Explanatory Models 1. Forecasting with Multiple Regression Causal Models
13 10/8 (Tuesday) Chapter 5 -- Explanatory Models 1. Forecasting with Multiple Regression Causal Models
14 10/10 (Thursday) Chapter 5 -- Explanatory Models 1. Forecasting with Multiple Regression Causal Models
15 10/15 (Tuesday) Chapter 6 -- Explanatory Models 2. Time-Series Decomposition
16 10/17 (Thursday) Chapter 6 -- Explanatory Models 2. Time-Series Decomposition
October 20 - 27 Fall Break - No Classes
17 10/29 (Tuesday) Chapter 7 -- Explanatory Models 3. ARIMA (Box-Jenkins) Forecasting Models
18 10/31 (Thursday) Chapter 8 -- Predictive Analytics: Helping to Make Sense of Big Data
19 11/5 (Tuesday) Chapter 8 -- Predictive Analytics: Helping to Make Sense of Big Data
20 11/7 (Thursday) Chapter 8 -- Predictive Analytics: Helping to Make Sense of Big Data
(Big Data, Datafication, Data Mining, The Role of Patterns, Categories of Analytics Tools, The Role of Correlation, Steps in the Data Mining Process, Overfitting, Diagnostic Statistics,
Lift, Confusion Matrices, Misclassification Rates, Receiver Operating Curves)
21 11/12 (Tuesday) -- Second Midterm Examination
22 11/14 (Thursday) Chapter 9 -- Classification Models: The Most Used Models in Analytics
(Classification, k-Nearest Neighbor, Classification Trees, Regression Trees, Naive Bayes, Logistics Regression)
23 11/19 (Tuesday) -- Chapter 9 -- Classification Models: The Most Used Models in Analytics
(Classification, k-Nearest Neighbor, Classification Trees, Regression Trees, Naive Bayes, Logistics Regression)
24 11/21 (Thursday) Chapter 10 -- Ensemble Models and Clustering
(Ensemble Learning, Bias, Bootstrap Aggregation - Bagging, Boosting, Random Forest, Clustering, k-Means Clustering, Hierarchical Clustering)
25 11/26 (Tuesday) -- Chapter 10 -- Ensemble Models and Clustering
(Ensemble Learning, Bias, Bootstrap Aggregation - Bagging, Boosting, Random Forest, Clustering, k-Means Clustering, Hierarchical Clustering)
November 27 - December 1 Thanksgiving Holiday - No Classes
26 12/3 (Tuesday) Chapter 11 -- Text Mining
(Text Mining, Bag of Words, Singular Value Decomposition, Zipf Plots, Scree Plots, Concept-Document Matrices, Natural Language Processing)
27 12/5 (Thursday) Chapter 11 -- Text Mining
(Text Mining, Bag of Words, Singular Value Decomposition, Zipf Plots, Scree Plots, Concept-Document Matrices, Natural Language Processing)
28 12/10 (Tuesday) Chapter 11 -- Text Mining
(Text Mining, Bag of Words, Singular Value Decomposition, Zipf Plots, Scree Plots, Concept-Document Matrices, Natural Language Processing)
29 12/12 (Thursday) Last Class Day -- Chapter 12 -- Forecast/Analytics Implementation
Final Examination for Forecasting & Predictive Analytics (FIN 40230):
December 16 - 20 (assigned by Registrar)
Location: TBD
Time: TBD
