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.
Introduction to Distance Measures for Time Series
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Skills You'll Learn
Dimensionality Reduction, Exploratory Data Analysis, Data Analysis, Time Series Analysis and Forecasting, Probability & Statistics, Feature Engineering, SAS (Software), Data Transformation, Statistical Analysis, Machine Learning Algorithms, Unsupervised Learning
From the lesson
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.