分离慢漂与快变信号，提升车载数据异常检测稳定性。
STREAM-VAE: Dual-Path Routing for Slow and Fast Dynamics in Vehicle Telemetry Anomaly Detection
- 双路编码器分别处理慢速漂移和快速波动信号
- 在真实车载数据集上异常检测准确率优于主流模型
- 适合车载监控与车队分析场景的实时部署
车载遥测数据同时包含缓慢漂移和快速波动，常在同一序列中混合出现，导致异常检测困难。传统基于重构的方法（如序列变分自编码器）采用单一潜在过程，混合异质时间尺度，易平滑波动或放大方差，削弱异常区分能力。本文提出STREAM-VAE，一种用于车载遥测时间序列异常检测的变分自编码器。模型采用双路编码器分离慢漂与快变信号动态，解码器将瞬时偏离独立于正常运行模式进行建模。该模型设计注重实际部署，可在不同工况下稳定输出异常评分，适用于车载监测与后端车队分析。在真实车载遥测数据集及公开SMD基准测试上，显式分离漂移与波动动态显著提升了鲁棒性，优于强基线方法（包括预测、注意力、图神经网络及VAE模型）。
原文摘要 · Abstract (English)
Automotive telemetry data exhibits slow drifts and fast spikes, often within the same sequence, making reliable anomaly detection challenging. Standard reconstruction-based methods, including sequence variational autoencoders (VAEs), use a single latent process and therefore mix heterogeneous time scales, which can smooth out spikes or inflate variances and weaken anomaly separation. In this paper, we present STREAM-VAE, a variational autoencoder for anomaly detection in automotive telemetry time-series data. Our model uses a dual-path encoder to separate slow drift and fast spike signal dynamics, and a decoder that represents transient deviations separately from the normal operating pattern. STREAM-VAE is designed for deployment, producing stable anomaly scores across operating modes for both in-vehicle monitors and backend fleet analytics. Experiments on an automotive telemetry dataset and the public SMD benchmark show that explicitly separating drift and spike dynamics improves robustness compared to strong forecasting, attention, graph, and VAE baselines.
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