The course covers the fundamentals of modeling time series data, and focuses on the applied use of the three main model types used to analyze univariate time series: exponential smoothing, autoregressive integrated moving average with exogenous variables (ARIMAX), and unobserved components (UCM).
Learn how to
- create time series data
- accommodate trend, as well as seasonal and event-related variation, in time series models
- diagnose, fit, and interpret exponential smoothing models, ARIMAX models, and unobserved components models
- identify relative strengths and weaknesses of the three model types.
Who should attend
Analysts with a quantitative background as well as non-statistical analysts and domain experts who would like to augment their time series modeling proficiency
Formats available | Standard Duration (duration can vary, see event schedule for details) | | |
Classroom: |
2.0 days | | |
Live Web Classroom: |
4 half-day session(s) |
e-Learning: |
14 hours/180 day license |
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Before attending this course, you should have an understanding of basic statistical concepts. You can gain this experience by completing the Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression course.
This course addresses SAS/ETS, SAS Studio software.