Prediction of rare events in the operation of household equipment using co-evolving time series
This addresses the challenge of imbalanced datasets for predicting equipment failures in homes, though it appears incremental.
The authors tackled the problem of predicting rare events like household equipment failures using co-evolving time series, and their approach outperformed state-of-the-art methods on synthetic and real-world datasets.
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.