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 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.
Overview
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.
- Motif Analysis
- In this module you learn about detecting motifs in times series and their usefulness.
- Course Review
Taught by
Chip Wells
