Forecasting Analytics

taught by Galit Shmueli

Brief Description:

This course will teach you how to choose an appropriate time series model, fit the model, to conduct diagnostics, and use the model for forecasting.

Instructor(s):
Level: Intermediate

Who Should Take This Course:

Business analysts, sales forecasters, economists, financial analysts, anyone who needs to produce, interpret or assess forecasts will find this course useful. Participants should be familiar with basic statistics, including linear regression.

Dates:
September 14, 2012 to October 12, 2012
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Registration:
Please read the syllabus tab, noting the prerequisites, text and software requirements.

Register Online -$499
Register Online -$399 (you must be affiliated with a college, university or high school)

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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. Multiple course registrations may be entitled to tuition discounts; read more.

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Forecasting Analytics

taught by Galit Shmueli

Aim of Course:

This course will teach you 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.

Prerequisite(s):

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

Course Program:

SESSION 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

SESSION 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

SESSION 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

SESSION 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.

Organization of the Course:

This course takes place over the internet 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.

The course typically requires 15 hours per week. 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.

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" eBook is here and hardcopy is 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.

Register Now

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Forecasting Analytics

taught by Galit Shmueli

Instructor(s):
Dates:
September 14, 2012 to October 12, 2012
Course Fee: $499
Academic Rate: $399

Before registering, please read the syllabus tab, noting the prerequisites, text and software requirements. When you click the register button, you will be taken to our secure transaction page.

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