This course focuses on data exploration, feature creation, and feature selection for time sequences. The topics discussed include binning, smoothing, transformations, and data set operations for time series, spectral analysis, singular spectrum analysis, distance measures, and motif analysis.
Este curso faz parte do Programa de cursos integrados Analyzing Time Series and Sequential Data
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Informações sobre o curso
oferecido por

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.
Programa - O que você aprenderá com este curso
Specialization Overview
In this module you get an overview of the courses in this specialization and what you can expect.
Course Overview
In this module you learn about the scope of this course and you access the software and files you will use for practices in the course.
Time Series Basics
In this module, you learn about converting transactional sequences to time series. Other topics include exploring signal components in time series via decompositions and binning, and creating new time series features.
Distance Measures
In this module you learn about the usefulness of distance or similarity measures between time series. Calculated distance measure are used as the basis in two analyses.
Sobre Programa de cursos integrados 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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