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.

Lernen Sie, wie Sie / 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.

Zielgruppe / 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

Verfügbare Schulungsformen / Formats available	Dauer (kann variieren, Details s. Terminliste)
Präsenzkurs:	2.0 Tage
Live Web Class:	14 Stunden	Systemvoraussetzungen
E-Learning:	14 Stunden/180 Tag Lizenz	Systemvoraussetzungen

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.

In diesem Kurs wird mit folgenden Software Modulen gearbeitet / 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.