[
    {
        "id": "osp-20449",
        "type": "article-journal",
        "title": "No Scale Left Behind: Multi-Scale Autoencoder with Bi-directional Attention for Time Series Anomaly Detection",
        "author": [
            {
                "family": "Guo",
                "given": "Jiaheng"
            },
            {
                "family": "Zhang",
                "given": "Haochen"
            },
            {
                "family": "Huang",
                "given": "Yu-Chao"
            },
            {
                "family": "Duan",
                "given": "Jinhao"
            },
            {
                "family": "Konz",
                "given": "Nicholas"
            },
            {
                "family": "Chen",
                "given": "Tianlong"
            }
        ],
        "URL": "https://omanscience.com/en/articles/no-scale-left-behind-multi-scale-autoencoder-with-bi-directional-attention-for-time-series-anomaly-detection",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Time series anomaly detection (TSAD) plays a crucial role in healthcare, finance, industrial monitoring, and other sectors. Within and between these settings, anomalies span vastly different temporal scales, from sub-second point spikes to multi-hour drift patterns. However, most existing TSAD methods commit to a single temporal granularity, and multi-scale designs either analyze different scales in isolation or are constrained to a predefined coarse-to-fine hierarchy, both failing to sufficiently capture multi-scale interactions. To resolve this limitation, we propose Multi-Scale Autoencoder with Cross-Scale Attention for TSAD (MSCAD), a simple yet powerful semi-supervised TSAD framework founded on parallel autoencoder branches corresponding to different patch sizes. A stack of symmetric bidirectional cross-scale attention blocks enables every pair of scales to exchange information before reconstruction without allowing any single scale to be privileged. On the comprehensive TSB-AD benchmark (40 datasets, 530 series), MSCAD achieves large performance gains against 50 baselines across multiple metrics, with VUS-PR of 0.57(+9.6%) on the univariate split and 0.47(+9.3%) on the multivariate split compared to the state-of-the-art."
    }
]