Geodesic
Detecting and eliminating covariate shifts in data for a more robust HDD failure prediction
Zapiski Nauchnykh Seminarov POMI, Investigations on applied mathematics and informatics. Part IV, Tome 540 (2024), pp. 148-161

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Prediction of HDD failures has garnered significant attention in research, yet the persistence of covariate shifts in data remains a practical challenge. In this work we introduce a novel approach to training covariate shift detection models without the need for additional real data or artificial shift modeling. Moreover, we propose a comprehensive methodology integrating shift detection, administrator alerts, shift elimination, and HDD failure prediction. Experimental results demonstrate the viability of our real-world implementation. 
@article{ZNSL_2024_540_a7,
     author = {K. Lukyanov and M. Drobyshevskiy and D. Turdakov},
     title = {Detecting and eliminating covariate shifts in data for a more robust {HDD} failure prediction},
     journal = {Zapiski Nauchnykh Seminarov POMI},
     pages = {148--161},
     publisher = {mathdoc},
     volume = {540},
     year = {2024},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/ZNSL_2024_540_a7/}
}
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K. Lukyanov; M. Drobyshevskiy; D. Turdakov. Detecting and eliminating covariate shifts in data for a more robust HDD failure prediction. Zapiski Nauchnykh Seminarov POMI, Investigations on applied mathematics and informatics. Part IV, Tome 540 (2024), pp. 148-161. http://geodesic.mathdoc.fr/item/ZNSL_2024_540_a7/