Time Series Anomaly Detection with Multiresolution Ensemble Decoding
| العنوان: | Time Series Anomaly Detection with Multiresolution Ensemble Decoding |
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| المؤلفون: | Lifeng Shen, Zhongzhong Yu, Qianli Ma, James T. Kwok |
| المصدر: | Proceedings of the AAAI Conference on Artificial Intelligence. 35:9567-9575 |
| بيانات النشر: | Association for the Advancement of Artificial Intelligence (AAAI), 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | General Medicine |
| الوصف: | Recurrent autoencoder is a popular model for time series anomaly detection, in which outliers or abnormal segments are identified by their high reconstruction errors. However, existing recurrent autoencoders can easily suffer from overfitting and error accumulation due to sequential decoding. In this paper, we propose a simple yet efficient recurrent network ensemble called Recurrent Autoencoder with Multiresolution Ensemble Decoding (RAMED). By using decoders with different decoding lengths and a new coarse-to-fine fusion mechanism, lower-resolution information can help long-range decoding for decoders with higher-resolution outputs. A multiresolution shape-forcing loss is further introduced to encourage decoders' outputs at multiple resolutions to match the input's global temporal shape. Finally, the output from the decoder with the highest resolution is used to obtain an anomaly score at each time step. Extensive empirical studies on real-world benchmark data sets demonstrate that the proposed RAMED model outperforms recent strong baselines on time series anomaly detection. |
| تدمد: | 2374-3468 2159-5399 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::43b74bfed9a6d173884902d17df25e85 https://doi.org/10.1609/aaai.v35i11.17152 |
| رقم الأكسشن: | edsair.doi...........43b74bfed9a6d173884902d17df25e85 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 23743468 21595399 |
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