Aura: Universal Multi-dimensional Exogenous Integration for Aviation Time Series

2026-03-05

AI Summary

This work addresses the challenge of modeling complex interactions between multidimensional, heterogeneous exogenous factors and target time series in aviation forecasting—a limitation that hinders the performance of unimodal approaches. Focusing on aircraft maintenance scenarios, the study systematically identifies three distinct interaction patterns between exogenous variables and time series for the first time, and introduces Aura, a general and plug-and-play fusion framework. Aura employs a tri-branch encoding mechanism to explicitly embed heterogeneous external information into mainstream time series models—such as Transformers and Temporal Convolutional Networks—according to their respective interaction patterns. Evaluated on a real-world three-year dataset from China Southern Airlines covering Boeing 777 and Airbus A320 fleets, Aura significantly outperforms existing baselines, achieving state-of-the-art performance and effectively enhancing aviation safety and reliability.

Technology Category

• Machine Learning: Multimodal Learning
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Time series forecasting has witnessed an increasing demand across diverse industrial applications, where accurate predictions are pivotal for informed decision-making. Beyond numerical time series data, reliable forecasting in practical scenarios requires integrating diverse exogenous factors. Such exogenous information is often multi-dimensional or even multimodal, introducing heterogeneous interactions that unimodal time series models struggle to capture. In this paper, we delve into an aviation maintenance scenario and identify three distinct types of exogenous factors that influence temporal dynamics through distinct interaction modes. Based on this empirical insight, we propose Aura, a universal framework that explicitly organizes and encodes heterogeneous external information according to its interaction mode with the target time series. Specifically, Aura utilizes a tailored tripartite encoding mechanism to embed heterogeneous features into well-established time series models, ensuring seamless integration of non-sequential context. Extensive experiments on a large-scale, three-year industrial dataset from China Southern Airlines, covering the Boeing 777 and Airbus A320 fleets, demonstrate that Aura consistently achieves state-of-the-art performance across all baselines and exhibits superior adaptability. Our findings highlight Aura's potential as a general-purpose enhancement for aviation safety and reliability.

Problem

• time series forecasting
• exogenous factors
• multimodal integration
• aviation maintenance
• heterogeneous interactions

Innovation

• exogenous integration
• time series forecasting
• heterogeneous interaction
• tripartite encoding
• aviation maintenance