Publication: Human-Based and Automatic Feature Ideation for Time Series Data: A Comparative Study
Human-Based and Automatic Feature Ideation for Time Series Data: A Comparative Study
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Schmidt, J., Piringer, H., Mühlbacher, T., & Bernard, J. (2023). Human-Based and Automatic Feature Ideation for Time Series Data: A Comparative Study. 7–12. https://doi.org/10.2312/eurova.20231089
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Feature ideation is a crucial early step in the feature extraction process, where new features are extracted from raw data. For phenomena existing in time series data, this often includes the ideation of statistical parameters, representations of trends and periodicity, or other geometrical and shape-based characteristics. The strengths of automatic feature ideation methods are their generalizability, applicability, and robustness across cases, whereas human-based feature ideation is most useful in uncharted real-world applications, w
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- Schmidt, Johanna
- Piringer, Harald
- Mühlbacher, Thomas
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Schmidt, J., Piringer, H., Mühlbacher, T., & Bernard, J. (2023). Human-Based and Automatic Feature Ideation for Time Series Data: A Comparative Study. 7–12. https://doi.org/10.2312/eurova.20231089
