arXiv Machine Learning By Jie Min, Yueyao Wang, Mengkun Chen

SSH-Net: A Deep Neural Network for Predicting Failure Time Distribution Functions under Competing Risks with Application to GPU Data

Read the original on arXiv Machine Learning →

arXiv:2606. 20451v1 Announce Type: cross Abstract: Competing risks are commonly observed in engineering fields and can bring challenges to time-to-event data modeling when the application scenarios are complicated.

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arXiv AI
Jun 4

HEPA: A Self-Supervised Horizon-Conditioned Event Predictive Architecture for Time Series

arXiv:2605. 11130v4 Announce Type: replace-cross Abstract: Critical events in multivariate time series, from turbine failures to cardiac arrhythmias, demand accurate prediction, yet labeled data is scarce because such events are rare and costly to annotate.

By Jonas Petersen, Gian-Alessandro Lombardi, Riccardo Maggioni, Camilla Mazzoleni, Federico Martelli, Philipp Petersen
arXiv Machine Learning
Sep 23

Event-Based Early Warning of Vineyard Disease Risk from Environmental Time Series

The paper proposes an event-based approach to predict transitions into vineyard disease‑risk periods within a 3–7 day window, rather than daily disease status. It defines events only after a minimum disease‑free gap to reduce label fragmentation and uses multi‑year agro‑meteorological data to capture humidity, rainfall, temperature, and seasonal patterns. Experiments with XGBoost, LSTM, and TCN models show that this formulation improves short‑horizon warning, highlighting trade‑offs among recall, lead time, and false alerts.

By Ivica Dimitrovski, Ivan Kitanovski, Danco Davcev, Slobodan Kalajdziski, Kosta Mitreski