Creating Features for Time Series Data

SAS via Coursera

Coursera based on 0 ratings

Likes

Overview

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. In this course you learn to perform motif analysis and implement analyses in the spectral or frequency domain. You also discover how distance measures work, implement applications, explore signal components, and create time series features. This course is appropriate for analysts with...

Syllabus

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.

Spectral Analysis and Singular Spectrum Analysis (SSA)

In this module, we discuss and illustrate the basic ideas and applications in frequency domain analysis. We also discuss SSA and present demonstrations of applied SSA.