arXiv:2605.11130v3 Announce Type: replace 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. We introduce HEPA (Horizon-conditioned Event Predictive Architecture), built on two key principles. First, a causal Transformer encoder is pretrained via a Joint-Embedding Predictive Architecture (JEPA): a horizon-conditioned predictor learns to forecast future representations rather than future values, forcing the encoder to capture predictable t
The proliferation of complex multivariate time series data across industries and the high cost of manual annotation necessitate advanced self-supervised learning methods for critical event prediction.
Accurate and automated prediction of rare critical events can significantly improve operational efficiency, safety, and decision-making in high-stakes environments.
This new architecture offers a more data-efficient and robust approach to predicting critical events in time series, potentially reducing reliance on extensive labeled datasets and human intervention.
- · Industrial IoT operators
- · Healthcare diagnostics
- · Predictive maintenance companies
- · AI/ML research institutions
- · Manual data annotation services
- · Traditional statistical forecasting models
- · Industries with high event-related downtime
Improved early warning systems reduce catastrophic failures and economic losses across various sectors.
Reduced operational costs and increased uptime lead to competitive advantages for early adopters of such predictive AI.
The widespread application of self-supervised time series prediction could catalyze further advancements in autonomous decision-making systems and intelligent infrastructure.
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Read at arXiv cs.LG