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.

翻译：汽车遥测数据通常在同一序列中同时表现出缓慢漂移和快速尖峰，这使得可靠的异常检测具有挑战性。标准的基于重构的方法，包括序列变分自编码器（VAE），使用单一的潜在过程，因此混合了异质的时间尺度，这可能会平滑尖峰或放大方差，从而削弱异常分离能力。本文提出STREAM-VAE，一种用于汽车遥测时间序列数据异常检测的变分自编码器。我们的模型采用双路径编码器来分离慢速漂移和快速尖峰信号动态，并使用一个解码器将瞬态偏差与正常运行模式分开表示。STREAM-VAE专为部署而设计，能为车载监控器和后端车队分析系统在各种运行模式下产生稳定的异常分数。在汽车遥测数据集和公开的SMD基准上的实验表明，与强大的预测、注意力、图模型和VAE基线相比，显式分离漂移和尖峰动态提高了模型的鲁棒性。