Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Wen, Haomin"'
Given the GPS coordinates of a large collection of human agents over time, how can we model their mobility behavior toward effective anomaly detection (e.g. for bad-actor or malicious behavior detection) without any labeled data? Human mobility and t
Externí odkaz:
http://arxiv.org/abs/2410.01281
Outlier detection (OD) has a vast literature as it finds numerous applications in environmental monitoring, cybersecurity, finance, and medicine to name a few. Being an inherently unsupervised task, model selection is a key bottleneck for OD (both al
Externí odkaz:
http://arxiv.org/abs/2409.05672
Autor:
Mao, Xiaowei, Lin, Yan, Guo, Shengnan, Chen, Yubin, Xian, Xingyu, Wen, Haomin, Xu, Qisen, Lin, Youfang, Wan, Huaiyu
Uncertainty quantification in travel time estimation (TTE) aims to estimate the confidence interval for travel time, given the origin (O), destination (D), and departure time (T). Accurately quantifying this uncertainty requires generating the most l
Externí odkaz:
http://arxiv.org/abs/2408.12809
Autor:
Lin, Yan, Wei, Tonglong, Zhou, Zeyu, Wen, Haomin, Hu, Jilin, Guo, Shengnan, Lin, Youfang, Wan, Huaiyu
Vehicle trajectories provide valuable movement information that supports various downstream tasks and powers real-world applications. A desirable trajectory learning model should transfer between different regions and tasks without retraining, thus i
Externí odkaz:
http://arxiv.org/abs/2408.15251
Autor:
Lin, Yan, Liu, Yichen, Zhou, Zeyu, Wen, Haomin, Zheng, Erwen, Guo, Shengnan, Lin, Youfang, Wan, Huaiyu
Vehicle trajectories provide crucial movement information for various real-world applications. To better utilize vehicle trajectories, it is essential to develop a trajectory learning approach that can effectively and efficiently extract rich semanti
Externí odkaz:
http://arxiv.org/abs/2408.04916
Autor:
Lin, Yan, Zhou, Zeyu, Liu, Yicheng, Lv, Haochen, Wen, Haomin, Li, Tianyi, Li, Yushuai, Jensen, Christian S., Guo, Shengnan, Lin, Youfang, Wan, Huaiyu
Spatiotemporal trajectories are sequences of timestamped locations, which enable a variety of analyses that in turn enable important real-world applications. It is common to map trajectories to vectors, called embeddings, before subsequent analyses.
Externí odkaz:
http://arxiv.org/abs/2407.12550
Autor:
Zou, Xingchen, Huang, Jiani, Hao, Xixuan, Yang, Yuhao, Wen, Haomin, Yan, Yibo, Huang, Chao, Liang, Yuxuan
Learning effective geospatial embeddings is crucial for a series of geospatial applications such as city analytics and earth monitoring. However, learning comprehensive region representations presents two significant challenges: first, the deficiency
Externí odkaz:
http://arxiv.org/abs/2405.14135
Autor:
Zhou, Zeyu, Lin, Yan, Wen, Haomin, Xu, Qisen, Guo, Shengnan, Hu, Jilin, Lin, Youfang, Wan, Huaiyu
Spatio-temporal trajectories are crucial in various data mining tasks. It is important to develop a versatile trajectory learning method that performs different tasks with high accuracy. This involves effectively extracting two core aspects of inform
Externí odkaz:
http://arxiv.org/abs/2405.12459
Autor:
Yang, Yiyuan, Jin, Ming, Wen, Haomin, Zhang, Chaoli, Liang, Yuxuan, Ma, Lintao, Wang, Yi, Liu, Chenghao, Yang, Bin, Xu, Zenglin, Bian, Jiang, Pan, Shirui, Wen, Qingsong
The study of time series is crucial for understanding trends and anomalies over time, enabling predictive insights across various sectors. Spatio-temporal data, on the other hand, is vital for analyzing phenomena in both space and time, providing a d
Externí odkaz:
http://arxiv.org/abs/2404.18886
Autor:
Liang, Yuxuan, Wen, Haomin, Nie, Yuqi, Jiang, Yushan, Jin, Ming, Song, Dongjin, Pan, Shirui, Wen, Qingsong
Time series analysis stands as a focal point within the data mining community, serving as a cornerstone for extracting valuable insights crucial to a myriad of real-world applications. Recent advances in Foundation Models (FMs) have fundamentally res
Externí odkaz:
http://arxiv.org/abs/2403.14735