In this online training course, learn about the "nuts and bolts" about forecasting analysis and gain hands-on experience using the most popular methods: regression models, smoothing methods and autoregressive models.Enroll in Forecasting Analytics
About This Online Course
This online training course from statistics.com focuses on the most popular business forecasting methods:
- Regression models
- Smoothing methods, including moving average and exponential smoothing
- Autoregressive models
We also delve into enhancements, such as second-layer models and ensembles and various issues encountered in practice.
This is a hands-on learning experience and, while any software capable of doing time series forecasting can be used, assignment support is offered for these two programs:
- XLMiner – data mining program available either for Windows versions (Microsoft Excel) or over the web. Course participants will have access to a low-cost license for the software.
- R – free statistical programming environment
This course utilizes either of these books (available on Amazon):
- Practical Time Series Forecasting: A Hands-on Guide, 2nd edition (Practical Analytics, 2016), Shmueli, G. and Lichtendahl, K.
- Practical Time Series Forecasting in R: A Hands-on Guide, 2nd edition (Practical Analytics, 2016), Shmueli, G. and Lichtendahl, K. (if you are using R)
Learners must purchase either book before starting the course.
What You Will Learn
- 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
Dr. Galit Shmueli is a distinguished professor, Institute of Service Science, College of Technology Management, at National Tsing Hua University, Taiwan. Her previous academic appointments include the SRITNE chaired professor of data analytics and associate professor, statistics and information systems, at the Indian School of Business, India, and associate professor of statistics, Department of Decision, Operations and Information Technologies, Smith School of Business, at the University of Maryland.
Dr. Shmueli's research has been published in statistics, information systems, and marketing literature. She received her Doctorate and Master of Science in Statistics, Technion, from the Israel Institute of Technology and her Bachelor of Arts in Statistics and Psychology from Haifa University, Israel.
Who Should Take This Course
This course is perfect for data scientists, data analysts, sales forecasters, marketing managers, accountants, economists, financial analysts, risk managers or anyone who needs to produce, interpret or assess forecasts. Participants should be familiar with basic statistics, including linear regression.
However, it is assumed that you are versed in statistics or have the equivalent understanding of topics covered in the statistics.com courses: Statistics 1 - Probability and Study Design and Statistics 2 - Inference and Association.
A record of completion will be issued, along with professional development credits in the form of continuing education units upon 50-percent completion.
In addition, a Credly badge to add to your LinkedIn profile will be issued upon 80-percent completion of this online training course.
This self-paced, online training course takes place at The Institute for Statistics Education at statistics.com for four weeks. During each session week, you can participate at times of your own choosing—there are no set times for the lessons. 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.
$649 (per person)
Register through FedLearn using the special promo code FedLearn22 and receive a five-percent discount on the original online course price.
Continuing Education Unit Credits
This online course provides 5.0 CEUs upon 50-percent completion.
This course is also recommended for 3.0 upper division college credits by the American Council on Education upon 80-percent completion.