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SAS 2020-2018 Time Series Feature Extraction

Feature extraction is the practice of enhancing machine learning by finding characteristics in the data that help solve a particular problem. For time series data, feature extraction can be performed using various time series analysis and decomposition techniques. In addition, features can be obtained by sequence comparison techniques such as dynamic time warping and by subsequence discovery techniques such as motif analysis. This paper surveys some of the time series feature extraction methods and demonstrates them through examples that use SAS/ETS® and SAS® Visual Forecasting software.