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20222026
most citedTEAM: Topological Evolution-aware Framework for Traffic Forecasting--Extended Version

19 citations · 50 across the 8 of their papers we have counts for

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6 papers · 1 filter

cs.LG2025

Universal Multi-Domain Translation via Diffusion Routers

Duc Kieu, Kien Do, Tuan Hoang +4

Multi-domain translation (MDT) aims to learn translations between multiple domains, yet existing approaches either require fully aligned tuples or can only handle domain pairs seen…

cs.LG202419 cited

TEAM: Topological Evolution-aware Framework for Traffic Forecasting--Extended Version

Duc Kieu, Tung Kieu, Peng Han +3

Due to the global trend towards urbanization, people increasingly move to and live in cities that then continue to grow. Traffic forecasting plays an important role in the intellig…

cs.LG20224 cited

A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis

Yan Zhao, Liwei Deng, Xuanhao Chen +7

The continued digitization of societal processes translates into a proliferation of time series data that cover applications such as fraud detection, intrusion detection, and energ…

cs.LG202218 cited

Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting--Full Version

Razvan-Gabriel Cirstea, Chenjuan Guo, Bin Yang +3

A variety of real-world applications rely on far future information to make decisions, thus calling for efficient and accurate long sequence multivariate time series forecasting. W…

cs.LG20223 cited

Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection---Extended Version

Tung Kieu, Bin Yang, Chenjuan Guo +4

Time series data occurs widely, and outlier detection is a fundamental problem in data mining, which has numerous applications. Existing autoencoder-based approaches deliver state-…

cs.LG20226 cited

Towards Spatio-Temporal Aware Traffic Time Series Forecasting--Full Version

Razvan-Gabriel Cirstea, Bin Yang, Chenjuan Guo +2

Traffic time series forecasting is challenging due to complex spatio-temporal dynamics time series from different locations often have distinct patterns; and for the same time seri…