18 citations · 41 across the 8 of their papers we have counts for
11 papers
AutoPINN: When AutoML Meets Physics-Informed Neural Networks
Xinle Wu, Dalin Zhang, Miao Zhang +5
Physics-Informed Neural Networks (PINNs) have recently been proposed to solve scientific and engineering problems, where physical laws are introduced into neural networks as prior…
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…
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…
Weakly-supervised Temporal Path Representation Learning with Contrastive Curriculum Learning -- Extended Version
Sean Bin Yang, Chenjuan Guo, Jilin Hu +3
In step with the digitalization of transportation, we are witnessing a growing range of path-based smart-city applications, e.g., travel-time estimation and travel path ranking. A…
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-…
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…