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.
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:
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.
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.
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.
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:
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.
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.
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:
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.