Forecasting Analytics

Forecasting Analytics

taught by Galit Shmueli


Close Popup

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.

Anticipated learning outcomes:

  • Visualize time series data
  • Understand the different components of time series data
  • Distinguish explanation from forecasting
  • Specify appropriate metrics to assess forecasting models
  • Use smoothing methods with time series data (moving average, exponential smoothing)
  • Adjust for seasonality
  • Use regression methods for forecasting
  • Account for autocorrelation
  • Distinguish real trend and patterns from random behavior


This course may be taken individually (one-off) or as part of a certificate program.
Course 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: Smoothing-Based Methods

  • Model-driven vs. data-driven methods
  • Centered and trailing Moving Average (MA)
  • Exponential Smoothing (simple, double, triple)
  • De-trending and seasonal adjustment
  • Differencing


WEEK 3: 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 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.

Forecasting Analytics

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.
You should be familiar with introductory statistics.  Try these self tests to check your knowledge.
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.

Time Requirement:
About 15 hours per week, at times of  your choosing.

Options for Credit and Recognition:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:
  1. No credit - You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
  2. Certificate - 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.
  3. CEUs and/or proof of completion - 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.
  4. Other options - Specializations, INFORMS CAP recognition, and academic (college) credit are available for some courses
College credit:
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 .
Course Text:

"Practical Time Series Forecasting" in eBook or hardcopy, or, if you are using R, "Practical Time Series Forecasting in R."  Those in South Asia can purchase the books online here.


This is a hands-on course, and, while any software capable of doing time series forecasting can be used, assignment support is offered for two programs:

1. XLMiner, a data mining program available either (a) for Windows versions of Excel or (b) over the web. Course participants will have access to a no-cost license for XLMiner.

2. R, a free statistical programming environment.

Be sure to choose the book that corresponds to your chosen software program.


For XLMiner users:  Course participants will have receive a no-cost license for XLMiner - this is a special version, for this course.  IMPORTANT:  Do NOT download the free trial version of XLMiner from (it may conflict with the special course version). 



March 23, 2018 to April 20, 2018 July 27, 2018 to August 24, 2018 November 23, 2018 to December 21, 2018 March 22, 2019 to April 19, 2019 July 26, 2019 to August 23, 2019 November 22, 2019 to December 20, 2019 March 20, 2020 to April 17, 2020 July 24, 2020 to August 21, 2020 November 20, 2020 to December 18, 2020

Forecasting Analytics


March 23, 2018 to April 20, 2018 July 27, 2018 to August 24, 2018 November 23, 2018 to December 21, 2018 March 22, 2019 to April 19, 2019 July 26, 2019 to August 23, 2019 November 22, 2019 to December 20, 2019 March 20, 2020 to April 17, 2020 July 24, 2020 to August 21, 2020 November 20, 2020 to December 18, 2020

Course Fee: $589

Do you meet course prerequisites? What about book & software? (Click here to learn more)

We have flexible policies to transfer to another course, or withdraw if necessary (modest fee applies)

Group rates: Click here to get information on group rates. 

First time student or academic? Click here for an introductory offer on select courses. Academic affiliation?  You may be eligible for a discount at checkout.

Register Now

Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment. Please use this printed registration form, for these and other special orders.

Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date, unless you specify otherwise.

The Institute for Statistics Education is certified to operate by the State Council of Higher Education in Virginia (SCHEV).

Want to be notified of future courses?

Student comments