KITE: A Knowledge-Injected Temporal Encoder for Time-Series Event Forecasting
Forecasting rare, high-cost events in multivariate time-series systems is hard because their precursors are seldom observed, so a purely data-driven model has little to learn from.
This paper introduces KITE, a novel temporal encoder for time-series event forecasting that integrates domain knowledge into self-supervised models to improve predictions of rare, high-cost events.
What happened
The article presents KITE, a model that enhances time-series forecasting by injecting semantic priors through switchable mechanisms. It demonstrates that these priors outperform baseline models in detecting rare events, particularly with limited labeled data.
Why it matters
Improving rare event forecasting is critical for high-stakes domains like finance, healthcare, and infrastructure monitoring, where early detection of low-frequency but high-impact events can prevent significant losses.
- Semantic priors can significantly enhance forecasting accuracy for rare events.
- Switchable mechanisms allow KITE to adapt to different types of domain knowledge without retraining.
- The model's out-of-domain transfer capability reduces the need for task-specific adjustments.
The bigger picture
KITE provides a scalable approach to incorporate domain knowledge into time-series models, potentially improving reliability in critical systems and reducing the need for extensive labeled datasets.
KITE offers a promising framework for integrating domain expertise into time-series forecasting, addressing limitations of purely data-driven approaches and enabling more robust predictions in high-stakes scenarios.
Technical details
- Covariate mechanism: Integrates external knowledge as additional features in the model.
- Attention-bias mechanism: Modifies attention weights to prioritize relevant prior information.
- Self-supervised learning framework: Reduces reliance on labeled data while leveraging domain expertise.
FAQ
What problem does KITE solve?
KITE addresses the challenge of forecasting rare, high-cost events in time-series data, where traditional models struggle due to insufficient precursor data.
How does KITE incorporate domain knowledge?
KITE uses two mechanisms—covariate injection and attention-bias adjustment—to integrate semantic priors into the model, enhancing its ability to detect events without requiring extensive labeled data.
What are the key results of the study?
KITE achieved significant improvements over baseline models, including a 14% increase in performance with periodicity priors and a 47% improvement with conservation residuals under low-label conditions.
This summary and analysis were generated with AI from the original article and may contain errors. Read the original for the full story.
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Originally published by arXiv cs.LG on Oct 12, 2026.
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