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.
Creating Features for Time Series Data
This course is part of Analyzing Time Series and Sequential Data Specialization
Instructor: Chip Wells
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There are 7 modules in this course
In this module you get an overview of the courses in this specialization and what you can expect.
What's included
1 video1 reading
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.
What's included
1 video2 readings1 app item
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.
What's included
13 videos4 assignments
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.
What's included
8 videos5 assignments1 app item
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.
What's included
11 videos6 assignments1 app item
In this module you learn about detecting motifs in times series and their usefulness.
What's included
6 videos2 assignments1 app item
What's included
1 assignment
Instructor
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Recommended if you're interested in Data Analysis
University of California, Santa Cruz
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