taught by Galit Shmueli
Aim of Course:
In this online course, “Forecasting Analytics,” you will learn how to choose an appropriate time series forecasting method, fit the model, evaluate its performance, and use it for forecasting. The course will focus on the most popular business forecasting methods: Regression models, smoothing methods including Moving Average (MA) and Exponential Smoothing, and Autoregressive (AR) models. It will also discuss enhancements such as second-layer models and ensembles, and various issues encountered in practice.
This course may be taken individually (one-off) or as part of a certificate program.
WEEK 1: Characterizing Time Series and the Forecasting Goal; Evaluating Predictive Accuracy and Data Partitioning
- Visualizing time series
- Time series components
- Forecasting vs. explanation
- Performance evaluation
- Naive forecasts
WEEK 2: Regression-Based Models
- Overview of forecasting methods
- Capturing trend seasonality and irregular patterns with linear regression
- Measuring and interpreting autocorrelation
- Evaluating predictability and the Random Walk
- Second-layer models using Autoregressive (AR) models
WEEK 3:Smoothing-Based Methods
- Model-driven vs. data-driven methods
- Centered and training Moving Average (MA)
- Exponential Smoothing (simple, double, triple)
- De-trending and seasonal adjustment
WEEK 4: Forecasting in Practice
- Forecasting implementation issues (automation, managerial forecast adjustments, and more)
- Communicating forecasts to stakeholders
- Overview of further forecasting methods (neural nets, ARIMA, and logistic regression)
- Forecasting binary outcomes
Homework in this course consists of short answer questions to test concepts, guided data analysis problems using software and guided data modeling problems using software.
In addition to assigned readings, this course also has an end of course data modeling project.
Who Should Take This Course:
Data Scientists, data analysts, sales forecasters, marketing managers, accountants, economists, financial analysts, risk managers, anyone who needs to produce, interpret or assess forecasts will find this course useful. Participants should be familiar with basic statistics, including linear regression.
Organization of the Course:
This course takes place online at the Institute for 4 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.
At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.
About 15 hours per week, at times of your choosing.
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:
- You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
- You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
- You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's). For those successfully completing the course, CEU's and a record of course completion will be issued by The Institute, upon request.
Forecasting Analytics has been evaluated by the American Council on Education (ACE) and is recommended for the lower-division baccalaureate/associate degree category, 3 semester hours in forecasting analytics, data mining or data science. Note: The decision to accept specific credit recommendations is up to each institution. More info here
This course is also recognized by the Institute for Operations Research and the Management Sciences (INFORMS) as helpful preparation for the Certified Analytics Professional (CAP®) exam, and can help CAP®analysts accrue Professional Development Units to maintain their certification .
"Practical Time Series Forecasting" in eBook or hardcopy. Those in South Asia can purchase the book onlinehere.
This is a hands-on course. Participants will apply forecasting algorithms to real data, and interpret the results. Course illustrations and homework assignments will use XLMiner, a data mining program available
- For Windows versions of Excel, or
- Over the web
Course participants will have access to a no-cost license for XLMiner, and teaching assistants will be able to offer feedback on assignments completed using XLMiner. Other forecasting programs may be used by participants, but support will not be available.
March 24, 2017 to April 21, 2017July 28, 2017 to August 25, 2017November 24, 2017 to December 22, 2017March 23, 2018 to April 20, 2018July 27, 2018 to August 24, 2018November 23, 2018 to December 21, 2018
Course Fee: $589