Time Series Modeling Essentials

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Not attempted Time Series Modeling Essentials

Duration: 14 hours

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

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/ETS;SAS Studio

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.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.

Live Instructor Dates SOLD SEPARATELY
DATES ▼	LOCATION ▲ ▼	TIME ▲ ▼	LANGUAGE	EVENT FEE
05-08 FEB 2024	Live Web, US	1:00 PM-4:30 PM EST	English	2,000 USD
24-27 JUN 2024	Live Web, US	1:00 PM-4:30 PM EDT	English	2,000 USD

Note: The lab for the English version of course has been updated to use a Hands-On Lab that is available immediately. If you started this course with the Hands-On Lab Reservation System, see Using SAS Studio with this Course in the Course Overview and follow the instructions to access the course files.

The hands-on lab is preconfigured to support this course and will not support hands-on practice for all your enrolled courses.

Hands-On Lab for Terminal Servers

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The hands-on lab is preconfigured to support this course and will not support hands-on practice for all your enrolled courses.

Hands-On Lab Reservation System

When you are planning your study time, keep in mind that the virtual lab takes 45-60 minutes to start

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Course Materials

e-Learning Materials

Practices
Demonstrations

Instructor-Led Training Materials

Course Notes
Course Data

Additional Learning

Video Tutorials

SAS Users Community

SAS Resource Hub

This course helps prepare you to earn the following SAS Credentials:

SAS Certified Specialist: SAS Text Analytics, Time Series, Experimentation and Optimization Using SAS 9.4
SAS Certified Advanced Analytics Professional Using SAS 9

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