activity
20182022
most citedEnsemble Grammar Induction For Detecting Anomalies in Time Series

2 citations · 4 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG2022

Robust Time Series Chain Discovery with Incremental Nearest Neighbors

Li Zhang, Yan Zhu, Yifeng Gao +1

Time series motif discovery has been a fundamental task to identify meaningful repeated patterns in time series. Recently, time series chains were introduced as an expansion of tim…

cs.LG2020

Semantic Discord: Finding Unusual Local Patterns for Time Series

Li Zhang, Yifeng Gao, Jessica Lin

Finding anomalous subsequence in a long time series is a very important but difficult problem. Existing state-of-the-art methods have been focusing on searching for the subsequence…

cs.LG20202 cited

Ensemble Grammar Induction For Detecting Anomalies in Time Series

Yifeng Gao, Jessica Lin, Constantin Brif

Time series anomaly detection is an important task, with applications in a broad variety of domains. Many approaches have been proposed in recent years, but often they require that…

cs.LG20192 cited

Discovering Subdimensional Motifs of Different Lengths in Large-Scale Multivariate Time Series

Yifeng Gao, Jessica Lin

Detecting repeating patterns of different lengths in time series, also called variable-length motifs, has received a great amount of attention by researchers and practitioners. Des…

cs.DS2018

Efficient Discovery of Variable-length Time Series Motifs with Large Length Range in Million Scale Time Series

Yifeng Gao, Jessica Lin

Detecting repeated variable-length patterns, also called variable-length motifs, has received a great amount of attention in recent years. Current state-of-the-art algorithm utiliz…