Abstract
Detecting anomalies in real-world multivariate time series data is challenging due to the deviation between the distributions of normal and anomalous data. Previous studies focused on capturing time and spatial features but lacked an effective criterion to measure differentiation from normal data. Our proposed method utilizes saliency detection, similar to anomaly detection, to identify the most significant region and effectively detect abnormal data. In this work, We propose a novel framework, Saliency-aware Anomaly Detection (SalAD), for detecting anomalies in multivariate time series data. SalAD comprises three main components: 1) a saliency detection module to remove redundant data, 2) an unsupervised saliency-aware forecasting model, and 3) a saliency-aware anomaly score to differentiate anomalies. We evaluate our model using the real-world Korea Aerospace Research Institute (KARI) orbital element dataset, which includes six orbital elements and unexpected disturbances from satellites, as well as conducting extensive experiments on four benchmark datasets to demonstrate its effectiveness and superiority over other baselines. The SalAD framework has been deployed on the K3A and K5 satellites.
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Acknowledgements
This work was partly supported by Institute for Information & communication Technology Planning & evaluation (IITP) grants funded by the Korean government MSIT: (No. 2022-0-01199, Graduate School of Convergence Security at Sungkyunkwan University) (No. 2022-0-01045, Self-directed Multi-Modal Intelligence for solving unknown, open domain problems) (No. 2022-0-00688, AI Platform to Fully Adapt and Reflect Privacy-Policy Changes) (No. 2021-0-02068, Artificial Intelligence Innovation Hub) (No. 2019-0-00421, AI Graduate School Support Program at Sungkyunkwan University), and (No. RS-2023-00230337, Advanced and Proactive AI Platform Research and Development Against Malicious Deepfakes). Lastly, this work was supported by Korea Internet & Security Agency (KISA) grant funded by the Korea government (PIPC) (No.RS-2023-00231200, Development of personal video information privacy protection technology capable of AI learning in an autonomous driving environment).
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Lee, S., Woo, S.S. (2024). Saliency-Aware Time Series Anomaly Detection for Space Applications. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14645. Springer, Singapore. https://doi.org/10.1007/978-981-97-2242-6_26
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