Prediction of rare events in the operation of household equipment using co-evolving time series

Autor:	Mecheri, Hadia, Benamirouche, Islam, Fass, Feriel, Ziou, Djemel, Kadri, Nassima
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	In this study, we propose an approach for predicting rare events by exploiting time series in coevolution. Our approach involves a weighted autologistic regression model, where we leverage the temporal behavior of the data to enhance predictive capabilities. By addressing the issue of imbalanced datasets, we establish constraints leading to weight estimation and to improved performance. Evaluation on synthetic and real-world datasets confirms that our approach outperform state-of-the-art of predicting home equipment failure methods.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2312.09410 Zobrazit plný text záznamu View this record from Arxiv