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.

Technology popularized by films like artificial intelligence, I Robot, and The Bicentennial Man, in which machines acquire human characteristics, Artificial Intelligence has emerged in recent years from the field of science fiction to real life. To get an idea of the industry's potential, according to a research study by Research and Markets, the artificial intelligence solutions market will drive more than $23 billion by 2025, with a 44 percent annual growth rate. For those who are not yet familiar with the term, it defines the area of computer science that aims to develop systems and equipment capable of simulating the human capacity to reason and consequently make decisions and perform tasks. At the last CES (Consumer Electronics Show) in Las Vegas, the world's largest technology fair, artificial intelligence was one of the highlights of the event, showing that the technology is expected to hit home appliances and electronics in 2018. Companies like LG and Samsung they announced at the event equipment such as TVs, refrigerators, stoves and washing machines with the ability to "talk" to each other, respond to voice commands and even learn from users' habits.