In this course you learn to build, refine, extrapolate, and, in some cases, interpret models designed for a single, sequential series. There are three modeling approaches presented. The traditional, Box-Jenkins approach for modeling time series is covered in the first part of the course. This presentation moves students from models for stationary data, or ARMA, to models for trend and seasonality, ARIMA, and concludes with information about specifying transfer function components in an ARIMAX, or time series regression, model. A Bayesian approach to modeling time series is considered next. The basic Bayesian framework is extended to accommodate autoregressive variation in the data as well as dynamic input variable effects. Machine learning algorithms for time series is the third approach. Gradient boosting and recurrent neural network algorithms are particularly well suited for accommodating nonlinear relationships in the data. Examples are provided to build intuition on the effective use of these algorithms.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Analyzing Time Series and Sequential Data
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Über diesen Kurs
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SAS
Through innovative software and services, SAS empowers and inspires customers around the world to transform data into intelligence. SAS is a trusted analytics powerhouse for organizations seeking immediate value from their data. A deep bench of analytics solutions and broad industry knowledge keep our customers coming back and feeling confident. With SAS®, you can discover insights from your data and make sense of it all. Identify what’s working and fix what isn’t. Make more intelligent decisions. And drive relevant change.
Lehrplan - Was Sie in diesem Kurs lernen werden
Specialization Overview (Review)
In this module you get an overview of the courses in this specialization and what you can expect.
Course Overview
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.
Introduction to Time Series
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.
ARIMAX Models
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.
Über den Spezialisierung Analyzing Time Series and Sequential Data
Using SAS Visual Forecasting and other SAS tools, you will learn to explore time series, create and select features, build and manage a large-scale forecasting system, and use a variety of models to identify, estimate and forecast signal components of interest.

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