SAS
Modeling Time Series and Sequential Data
SAS

Modeling Time Series and Sequential Data

This course is part of Analyzing Time Series and Sequential Data Specialization

Taught in English

Chip Wells
Ari Zitin
Danny Modlin

Instructors: Chip Wells

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Course

Gain insight into a topic and learn the fundamentals

Intermediate level
Some related experience required
10 hours (approximately)
Flexible schedule
Learn at your own pace

Details to know

Shareable certificate

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Assessments

19 quizzes

Course

Gain insight into a topic and learn the fundamentals

Intermediate level
Some related experience required
10 hours (approximately)
Flexible schedule
Learn at your own pace

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Build your subject-matter expertise

This course is part of the Analyzing Time Series and Sequential Data Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There are 8 modules in this course

In this module you get an overview of the courses in this specialization and what you can expect.

What's included

1 video1 reading

In this module, you get an idea of the scope of this course and learn to use SAS Viya for Learners to do the practices in the course.

What's included

1 video2 readings1 app item

This module reviews fundamental time series ideas. You learn about the basic components of systematic variation in time series data and some simple model specifications, such as the autoregressive order one and the random walk. You also learn about Exponential smoothing models or ESMs, selecting a champion ESM, and generating forecasts on time series.

What's included

11 videos2 quizzes1 app item

This module has four parts. The first part describes traditional models for stationary data: Auto Regressive Moving Average or ARMA models. The second part describes how the ARMA framework is generalized to accommodate trend variation. This involves integration, and results in the ARIMA model. The third part describes how the ARIMA model is adapted to handle seasonal variation in the data. The fourth and final part of the module introduces the dynamic regression or ARIMAX model and describes concepts related to identifying transfer function components and specifying ARIMAX models.

What's included

26 videos2 quizzes1 app item

In this module, we combine the worlds of time series and Bayesian analysis. We begin with a brief review of Bayesian analysis. We then explore how to incorporate autoregressive, seasonal, and exogenous components in a Bayesian time series. We conclude with a discussion on Bayesian scoring and posterior predictive distributions.

What's included

10 videos8 quizzes1 app item

In this module you learn how to use SAS machine learning tools to forecast individual time series. You learn to prepare the time series data for use with the machine learning tools, and how to build and score forecasting models using these tools. We focus on gradient boosting and recurrent neural network models and discuss when it would be useful to use these methods.

What's included

8 videos1 reading5 quizzes1 app item

This module describes how forecasts that are generated externally to the forecasting system can be accommodated in SAS Visual Forecasting. We'll use external forecasts to create a combined or ensemble forecast that has the potential to improve forecast precision relative to the constituent, external forecasts. This module concludes with a discussion of hybrid model forecasts that combine traditional and machine learning approaches to forecasting.

What's included

9 videos1 quiz1 app item

What's included

1 quiz

Instructors

Chip Wells
SAS
2 Courses1,974 learners
Ari Zitin
SAS
2 Courses4,729 learners
Danny Modlin
SAS
1 Course1,120 learners

Offered by

SAS

Recommended if you're interested in Data Analysis

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