60 citations · 108 across the 7 of their papers we have counts for
7 papers
OneShotSTL: One-Shot Seasonal-Trend Decomposition For Online Time Series Anomaly Detection And Forecasting
Xiao He, Ye Li, Jian Tan +2
Seasonal-trend decomposition is one of the most fundamental concepts in time series analysis that supports various downstream tasks, including time series anomaly detection and for…
A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning
Xinyi Zhang, Zhuo Chang, Hong Wu +5
Recently using machine learning (ML) based techniques to optimize modern database management systems has attracted intensive interest from both industry and academia. With an objec…
Interactive Log Parsing via Light-weight User Feedback
Liming Wang, Hong Xie, Ye Li +2
Template mining is one of the foundational tasks to support log analysis, which supports the diagnosis and troubleshooting of large scale Web applications. This paper develops a hu…
LPC-AD: Fast and Accurate Multivariate Time Series Anomaly Detection via Latent Predictive Coding
Zhi Qi, Hong Xie, Ye Li +3
This paper proposes LPC-AD, a fast and accurate multivariate time series (MTS) anomaly detection method. LPC-AD is motivated by the ever-increasing needs for fast and accurate MTS…
Towards Dynamic and Safe Configuration Tuning for Cloud Databases
Xinyi Zhang, Hong Wu, Yang Li +3
Configuration knobs of database systems are essential to achieve high throughput and low latency. Recently, automatic tuning systems using machine learning methods (ML) have shown…
Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation
Xinyi Zhang, Zhuo Chang, Yang Li +4
Recently, using automatic configuration tuning to improve the performance of modern database management systems (DBMSs) has attracted increasing interest from the database communit…
RobustTrend: A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend Filtering
Qingsong Wen, Jingkun Gao, Xiaomin Song +2
Extracting the underlying trend signal is a crucial step to facilitate time series analysis like forecasting and anomaly detection. Besides noise signal, time series can contain no…