Time Series Modeling Essentials
STSM : STSM51
This course discusses the fundamentals of modeling time series data. The course 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).
The e-learning format of this course includes Virtual Lab time to practice.
The e-learning format of this course includes Virtual Lab time to practice.
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
Prerequisites
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.
SAS Products Covered
SAS Studio;SAS/ETS
Course Outline
Introduction to Time Series
- Defining a time series.
- Using the TIMESERIES procedure to transform transactional data into time series data.
- Defining and exploring the systematic components in a time series.
- Describing the decomposition of time series variation.
- Listing three families of time series models.
- Introducing SAS Studio.
- Introducing the concepts of white noise and autocorrelation.
- Exploring weighted average models and exponential smoothing.
- Comparing and contrasting simple mean, random walk, and exponential smoothing models.
- Imputing missing values within a time series.
- Differentiating between ARMA and ARIMA models.
- Defining a stationary time series and identifying its importance.
- Describing and identifying autoregressive and moving average processes.
- Defining the differences between a random walk series, a white noise series, and an autoregressive (AR) series.
- Estimating autoregressive parameters .
- ARMAX and time series regression.
- Accuracy and forecasting of ARIMAX.
- Introducing unobserved components models (UCM) and focus on the multiple sources of error and parameters as a function of time.
- Describing the basic component models: level, slope, seasonal.
- Exploring the UCM model parameters.
- Running a UCM model using the UCM procedure.
- Defining Random Walk and Linear Trend series.
- Building a UCM model.
Live Class Schedule
Duration: 14 hours
Step into our live classes and experience a dynamic learning environment where you can ask questions, share ideas, and connect with your instructor and classmates. With on-demand lab hours, you can explore the material at your own pace. Our globally acclaimed instructors will motivate you to think bigger, so you can take what you've learned and achieve your biggest goals.
Live Class Schedule
SOLD SEPARATELY
Private Training
Get training tailored specifically for your team, led by expert SAS instructors. Choose from virtual sessions, or training at your location (or ours). Perfect for teams seeking a customized curriculum and plenty of interaction with a SAS specialist. We'll schedule it at a time that works for you.
Mentoring Services
Take your training to the next level with personalized mentoring. While private training offers structured coursework, mentoring provides hands-on, real-time support from a subject matter expert. As you work with your own data, you'll receive expert guidance to help you uncover insights, unlock the full potential of your data, and make faster progress. Perfect for those looking to apply what they’ve learned and see quicker results.