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

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.

Figures (8)

• Figure 1: Illustration of diverse exogenous information influencing aircraft component anomaly detection. Beyond the time series directly associated with the equipment, this detection process incorporates multiple factors, including correlated sensor signals, aircraft register, airport location, and dynamic environmental takeoff conditions, which collectively influence the temporal dynamics, necessitating a unified integration for accurate health monitoring.
• Figure 2: Bleed Air System Schematic.
• Figure 3: Overall architecture of Aura, a multi-dimensional exogenous integration framework. Aura integrates static attributes encoded by LLMs and geospatial embeddings, and prepends them to endogenous tokens. Two cross-attention layers fuse exogenous series via residual connections to regulate the strength of integration. A Mixture of Experts (MoE) module leverages LLM-generated future insights from dynamic events to guide time series forecasting.
• Figure 4: Statistical distribution of gated weights for history and future exogenous series.
• Figure 5: Deployed model inference visualization within the health monitoring system of China Southern Airlines.