This IBM Self-Paced Virtual Class (SPVC) includes:
- PDF course guide available to attendee during and after course
- Lab environment where students can work through demonstrations and exercises at their own pace
Contains PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.
This course gets you up and running with a set of procedures for analyzing time series data. Learn how to forecast using a variety of models, including regression, exponential smoothing, and ARIMA, which take into account different combinations of trend and seasonality. The Expert Modeler features will be covered, which is designed to automatically select the best fitting exponential smoothing or ARIMA model, but you will also learn how to specify your own custom models, and also how to identify ARIMA models yourself using a variety of diagnostic tools such as time plots and autocorrelation plots.
If you are enrolling in a Self Paced Virtual Classroom or Web Based Training course, before you enroll, please review the Self-Paced Virtual Classes and Web-Based Training Classes on our Terms and Conditions page, as well as the system requirements, to ensure that your system meets the minimum requirements for this course.
Please refer to course overview
Who Can Benefit
Roles: Business Analyst, Data Scientist
Specifically, this is an introductory course for:
- Anyone who is interested in getting up to speed quickly and efficiently using the IBM SPSS Modeler forecasting capabilities
- Familiarity with the IBM SPSS Modeler environment (creating, editing, opening, and saving streams).
- General knowledge of regression analysis is recommended but not required