On data efficiency of univariate time series anomaly detection models
Abstract In machine learning (ML) problems, it is widely believed that more training samples lead to improved predictive accuracy but incur higher computational costs. Consequently, achieving better data efficiency, that is, the trade-off between the size of the training set and the accuracy of the...
Αποθηκεύτηκε σε:
| Κύριοι συγγραφείς: | , , , , , |
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| Μορφή: | Artigo |
| Γλώσσα: | Inglês |
| Έκδοση: |
SpringerOpen
2024-06-01
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| Σειρά: | Journal of Big Data |
| Θέματα: | |
| Διαθέσιμο Online: | https://doi.org/10.1186/s40537-024-00940-7 |
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