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Forecasting Analytics

Instructor(s):

Dates:

March 27, 2015 to April 24, 2015 July 31, 2015 to August 28, 2015 November 27, 2015 to December 25, 2015 March 25, 2016 to April 22, 2016 July 29, 2016 to August 26, 2016 November 25, 2016 to December 23, 2016 March 24, 2017 to April 21, 2017 July 28, 2017 to August 25, 2017 November 24, 2017 to December 22, 2017 March 23, 2018 to April 20, 2018 July 27, 2018 to August 24, 2018 November 23, 2018 to December 21, 2018

Thank you for your submission.

Forecasting Analytics

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.

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: 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
  • Differencing

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:

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

Instructor(s):

Dates:
March 27, 2015 to April 24, 2015 July 31, 2015 to August 28, 2015 November 27, 2015 to December 25, 2015 March 25, 2016 to April 22, 2016 July 29, 2016 to August 26, 2016 November 25, 2016 to December 23, 2016 March 24, 2017 to April 21, 2017 July 28, 2017 to August 25, 2017 November 24, 2017 to December 22, 2017 March 23, 2018 to April 20, 2018 July 27, 2018 to August 24, 2018 November 23, 2018 to December 21, 2018

Course Fee: $589

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

Tuition Savings:  When you register online for 3 or more courses, $200 is automatically deducted from the total tuition. (This offer cannot be combined and is only applicable to courses of 3 weeks or longer.)

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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.

Forecasting Analytics

taught by Galit Shmueli

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.

Level:

Intermediate

Prerequisite:
These are listed for your benefit so you can determine for yourself, whether you have the needed background, whether from taking the listed courses, or by other experience.

If you are unclear as to whether you have mastered the above requirements, try these placement tests.

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.


Credit:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:
  1. You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
  2. 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. 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, 5.0 CEU's and a record of course completion will be issued by The Institute, upon request.

Course Text:

"Practical Time Series Forecasting" in eBook or hardcopy.  Those in South Asia can purchase the book online here.

Software:

Participants will need access to software that can do time series analysis. Examples and illustrations will be provided for XLMiner (an Excel add-in). The homework can all be done with XLMiner (a few exercises can be done just with Excel). (Most standard statistical software will be able to handle the majority of the procedures covered in this course.) Our teaching assistants can offer help with Minitab, XLMiner, JMP, Stata, and SAS. For information about software that is available at no charge, or nominal charge, for Statistics.com courses click here.


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