This comprehensive course on time series data exploration and feature creation is designed for analysts with a quantitative background and domain experts looking to expand their skill set. Throughout the course, you will delve into techniques such as binning, smoothing, spectral analysis, singular spectrum analysis, distance measures, and motif analysis, equipping you with the knowledge to implement analyses in the spectral or frequency domain and create time series features.
With a focus on practical application, the course covers a range of modules, including Time Series Basics, Distance Measures, Spectral Analysis and Singular Spectrum Analysis (SSA), and Motif Analysis. Each module provides in-depth learning and hands-on practice, ensuring you gain a strong understanding of key concepts and methodologies.
Whether you're interested in enhancing your expertise in time series data or augmenting your analytical capabilities, this course offers valuable insights and practical skills to help you excel in your data analysis endeavors.
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This course comprises modules covering a range of topics including Time Series Basics, Distance Measures, Spectral Analysis, Singular Spectrum Analysis, and Motif Analysis.
This module provides an overview of the course, including detailed information on its content and requirements. It also highlights the benefits and learning outcomes of the course.
Find out about the course instructor, review prerequisites, and learn how to access the course files for practice. Gain insights into using SAS Viya for Learners for demos and practices.
Explore the basics of time series data, understand summary measures, learn about binning, signal versus noise, and practice various time series analysis techniques.
Learn about distance measures for time series, including sequence similarity, time warping, and symbolic representation of sequences. Gain practical experience in performing similarity analysis and clustering.
Delve into spectral analysis fundamentals, regression approach to cycle identification, spectral density estimation, and singular spectrum analysis. Practice performing and interpreting Fourier analysis and singular spectrum analysis.
Discover motif analysis basics, explore motif discovery using different approaches, and learn about motif scoring. Practice scoring tables to reinforce your understanding of motif analysis.
This module provides a comprehensive review of the course, summarizing key learnings and concepts covered throughout the modules.
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