no code implementations • 11 Oct 2023 • Chenguo Lin, Xumeng Wen, Wei Cao, Congrui Huang, Jiang Bian, Stephen Lin, Zhirong Wu
In this work, we make key technical contributions that are tailored to the numerical properties of time-series data and allow the model to scale to large datasets, e. g., millions of temporal sequences.
no code implementations • 2 Jul 2023 • Ziyue Li, Yuchen Fang, You Li, Kan Ren, Yansen Wang, Xufang Luo, Juanyong Duan, Congrui Huang, Dongsheng Li, Lili Qiu
A timely detection of seizures for newborn infants with electroencephalogram (EEG) has been a common yet life-saving practice in the Neonatal Intensive Care Unit (NICU).
no code implementations • 30 Dec 2022 • Hong Guo, Yujing Wang, Jieyu Zhang, Zhengjie Lin, Yunhai Tong, Lei Yang, Luoxing Xiong, Congrui Huang
Time-series anomaly detection is an important task and has been widely applied in the industry.
2 code implementations • 19 Jun 2021 • Zhihan Yue, Yujing Wang, Juanyong Duan, Tianmeng Yang, Congrui Huang, Yunhai Tong, Bixiong Xu
Furthermore, to obtain the representation of an arbitrary sub-sequence in the time series, we can apply a simple aggregation over the representations of corresponding timestamps.
2 code implementations • 4 Sep 2020 • Hang Zhao, Yujing Wang, Juanyong Duan, Congrui Huang, Defu Cao, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, Qi Zhang
Anomaly detection on multivariate time-series is of great importance in both data mining research and industrial applications.
no code implementations • 25 Aug 2020 • Yuanxiang Ying, Juanyong Duan, Chunlei Wang, Yujing Wang, Congrui Huang, Bixiong Xu
The task is challenging because of the complex characteristics of time-series, which are messy, stochastic, and often without proper labels.
3 code implementations • 10 Jun 2019 • Hansheng Ren, Bixiong Xu, Yujing Wang, Chao Yi, Congrui Huang, Xiaoyu Kou, Tony Xing, Mao Yang, Jie Tong, Qi Zhang
At Microsoft, we develop a time-series anomaly detection service which helps customers to monitor the time-series continuously and alert for potential incidents on time.