Creating Features for Time Series Data (Coursera)

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 a quantitative background as well as domain experts who would like to augment their time-series tool box. Before taking this course, you should be comfortable with basic statistical concepts. You can gain this experience by completing the Statistics with SAS course. Familiarity with matrices and principal component analysis are also helpful but not required.
Course 1 of 3 in the Analyzing Time Series and Sequential Data Specialization.

Syllabus

WEEK 1
Specialization Overview
In this module you get an overview of the courses in this specialization and what you can expect.

WEEK 2
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.

WEEK 3
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.

WEEK 4
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.

WEEK 5
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.

WEEK 6
Motif Analysis
In this module you learn about detecting motifs in times series and their usefulness.

WEEK 7
Course Review