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

arXiv: https://arxiv.org/abs/2312.09410 · HTML: https://mlsanity.com/paper/2312.09410/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 19 references that are in the map.
- First posted on arXiv 15 Dec 2023.

## Built on by (most cited first, 2 of 2)

- [A Transferable Autologistic Model for Predicting Rare Failures in Heterogeneous Equipment](https://mlsanity.com/paper/2608.06695/) (2026)
- [Marker-free eye-gaze estimation using a single image and depth from defocus](https://mlsanity.com/paper/2609.09610/) (2026)

## Abstract (arXiv)

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.