taught by Galit Shmueli
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.
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 training Moving Average (MA)
- Exponential Smoothing (simple, double, triple)
- De-trending and seasonal adjustment
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.
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.
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:
- No credit - You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
- 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.
- 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.
- Other options - Statistics.com Specializations, INFORMS CAP recognition, and academic (college) credit are available for some Statistics.com courses
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 solver.com (it may conflict with the special course version).
November 23, 2018 to December 21, 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)
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.
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).
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