Search Results for author: Congrui Huang

Found 7 papers, 3 papers with code

NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time Series Pretraining

no code implementations11 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.

Learning Semantic Representations Temporal Sequences +1

Protecting the Future: Neonatal Seizure Detection with Spatial-Temporal Modeling

no code implementations2 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).

EEG Electroencephalogram (EEG) +1

TS2Vec: Towards Universal Representation of Time Series

2 code implementations19 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.

Contrastive Learning Time Series +3

Multivariate Time-series Anomaly Detection via Graph Attention Network

2 code implementations4 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.

Anomaly Detection Graph Attention +3

Automated Model Selection for Time-Series Anomaly Detection

no code implementations25 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.

Anomaly Detection Model Selection +2

Time-Series Anomaly Detection Service at Microsoft

3 code implementations10 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.

Anomaly Detection Saliency Detection +2

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