Time Series Modeling Essentials

Course code: 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.

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Course dates

Starting date: Upon request

Type: E-learning

Course duration: 21 hours

Language: en

Price without VAT: 720 EUR


Starting date: Upon request

Type: Upon request

Course duration: 14 hours

Language: en

Price without VAT: 1 200 EUR


Type Course
Language Price without VAT
Upon request E-learning 21 hours en 720 EUR Register
Upon request Upon request 14 hours en 1 200 EUR Register
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Target group

Analysts with a quantitative background as well as non-statistical analysts and domain experts who would like to augment their time series modeling proficiency

Course structure

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.


  • 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.


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.

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