Time Series Modeling Essentials

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.

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	Duration
Live Web Classroom:	4 half-day session(s)
e-Learning:	14 hours/180 day license

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.

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.

Exponential Smoothing Models

• 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.

ARIMAX Models

• 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.

Unobserved Components Models

• 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.