27 citations · 76 across the 14 of their papers we have counts for
5 papers · 1 filter
Can LLMs Serve As Time Series Anomaly Detectors?
Manqing Dong, Hao Huang, Longbing Cao
An emerging topic in large language models (LLMs) is their application to time series forecasting, characterizing mainstream and patternable characteristics of time series. A relev…
Refining the Optimization Target for Automatic Univariate Time Series Anomaly Detection in Monitoring Services
Manqing Dong, Zhanxiang Zhao, Yitong Geng +3
Time series anomaly detection is crucial for industrial monitoring services that handle a large volume of data, aiming to ensure reliability and optimize system performance. Existi…
AutoSmart: An Efficient and Automatic Machine Learning framework for Temporal Relational Data
Zhipeng Luo, Zhixing He, Jin Wang +4
Temporal relational data, perhaps the most commonly used data type in industrial machine learning applications, needs labor-intensive feature engineering and data analyzing for giv…
Deep Neural Network Hyperparameter Optimization with Orthogonal Array Tuning
Xiang Zhang, Xiaocong Chen, Lina Yao +2
Deep learning algorithms have achieved excellent performance lately in a wide range of fields (e.g., computer version). However, a severe challenge faced by deep learning is the hi…
GrCAN: Gradient Boost Convolutional Autoencoder with Neural Decision Forest
Manqing Dong, Lina Yao, Xianzhi Wang +2
Random forest and deep neural network are two schools of effective classification methods in machine learning. While the random forest is robust irrespective of the data domain, th…