Zobrazeno 1 - 10
of 467
pro vyhledávání: '"Fan Yujie"'
Autor:
Yeh, Chin-Chia Michael, Der, Audrey, Saini, Uday Singh, Lai, Vivian, Zheng, Yan, Wang, Junpeng, Dai, Xin, Zhuang, Zhongfang, Fan, Yujie, Chen, Huiyuan, Aboagye, Prince Osei, Wang, Liang, Zhang, Wei, Keogh, Eamonn
The Matrix Profile (MP), a versatile tool for time series data mining, has been shown effective in time series anomaly detection (TSAD). This paper delves into the problem of anomaly detection in multidimensional time series, a common occurrence in r
Externí odkaz:
http://arxiv.org/abs/2409.09298
Autor:
Wang, Liang, Jain, Shubham, Dou, Yingtong, Wang, Junpeng, Yeh, Chin-Chia Michael, Fan, Yujie, Aboagye, Prince, Zheng, Yan, Dai, Xin, Zhuang, Zhongfang, Saini, Uday Singh, Zhang, Wei
Numerous algorithms have been developed for online product rating prediction, but the specific influence of user and product information in determining the final prediction score remains largely unexplored. Existing research often relies on narrowly
Externí odkaz:
http://arxiv.org/abs/2409.04649
Autor:
Der, Audrey, Yeh, Chin-Chia Michael, Dai, Xin, Chen, Huiyuan, Zheng, Yan, Fan, Yujie, Zhuang, Zhongfang, Lai, Vivian, Wang, Junpeng, Wang, Liang, Zhang, Wei, Keogh, Eamonn
Self-supervised Pretrained Models (PTMs) have demonstrated remarkable performance in computer vision and natural language processing tasks. These successes have prompted researchers to design PTMs for time series data. In our experiments, most self-s
Externí odkaz:
http://arxiv.org/abs/2408.07869
Autor:
Yeh, Chin-Chia Michael, Fan, Yujie, Dai, Xin, Saini, Uday Singh, Lai, Vivian, Aboagye, Prince Osei, Wang, Junpeng, Chen, Huiyuan, Zheng, Yan, Zhuang, Zhongfang, Wang, Liang, Zhang, Wei
Spatial-temporal forecasting systems play a crucial role in addressing numerous real-world challenges. In this paper, we investigate the potential of addressing spatial-temporal forecasting problems using general time series forecasting models, i.e.,
Externí odkaz:
http://arxiv.org/abs/2402.10487
Autor:
Aboagye, Prince, Zheng, Yan, Wang, Junpeng, Saini, Uday Singh, Dai, Xin, Yeh, Michael, Fan, Yujie, Zhuang, Zhongfang, Jain, Shubham, Wang, Liang, Zhang, Wei
The emergence of pre-trained models has significantly impacted Natural Language Processing (NLP) and Computer Vision to relational datasets. Traditionally, these models are assessed through fine-tuned downstream tasks. However, this raises the questi
Externí odkaz:
http://arxiv.org/abs/2401.02987
Autor:
Yeh, Chin-Chia Michael, Chen, Huiyuan, Fan, Yujie, Dai, Xin, Zheng, Yan, Lai, Vivian, Wang, Junpeng, Zhuang, Zhongfang, Wang, Liang, Zhang, Wei, Keogh, Eamonn
Time series classification is a widely studied problem in the field of time series data mining. Previous research has predominantly focused on scenarios where relevant or foreground subsequences have already been extracted, with each subsequence corr
Externí odkaz:
http://arxiv.org/abs/2311.02561
Autor:
Yeh, Chin-Chia Michael, Chen, Huiyuan, Dai, Xin, Zheng, Yan, Fan, Yujie, Lai, Vivian, Wang, Junpeng, Der, Audrey, Zhuang, Zhongfang, Wang, Liang, Zhang, Wei
Time series data is ubiquitous across various domains such as finance, healthcare, and manufacturing, but their properties can vary significantly depending on the domain they originate from. The ability to perform Content-based Time Series Retrieval
Externí odkaz:
http://arxiv.org/abs/2311.02560
Autor:
Zhang, Dongyu, Wang, Liang, Dai, Xin, Jain, Shubham, Wang, Junpeng, Fan, Yujie, Yeh, Chin-Chia Michael, Zheng, Yan, Zhuang, Zhongfang, Zhang, Wei
Sequential tabular data is one of the most commonly used data types in real-world applications. Different from conventional tabular data, where rows in a table are independent, sequential tabular data contains rich contextual and sequential informati
Externí odkaz:
http://arxiv.org/abs/2310.13818
Autor:
Yeh, Chin-Chia Michael, Dai, Xin, Zheng, Yan, Wang, Junpeng, Chen, Huiyuan, Fan, Yujie, Der, Audrey, Zhuang, Zhongfang, Wang, Liang, Zhang, Wei
Multitask learning (MTL) aims to develop a unified model that can handle a set of closely related tasks simultaneously. By optimizing the model across multiple tasks, MTL generally surpasses its non-MTL counterparts in terms of generalizability. Alth
Externí odkaz:
http://arxiv.org/abs/2310.03925
Autor:
Yeh, Chin-Chia Michael, Chen, Huiyuan, Dai, Xin, Zheng, Yan, Wang, Junpeng, Lai, Vivian, Fan, Yujie, Der, Audrey, Zhuang, Zhongfang, Wang, Liang, Zhang, Wei, Phillips, Jeff M.
A Content-based Time Series Retrieval (CTSR) system is an information retrieval system for users to interact with time series emerged from multiple domains, such as finance, healthcare, and manufacturing. For example, users seeking to learn more abou
Externí odkaz:
http://arxiv.org/abs/2310.03919