5 citations · 9 across the 4 of their papers we have counts for
7 papers
Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting
Yingtao Luo, Shikai Fang, Binqing Wu +2
Weather forecasting is essential but remains computationally intensive and physically incomplete in traditional numerical weather prediction (NWP) methods. Deep learning (DL) model…
Noise-Resilient Point-wise Anomaly Detection in Time Series Using Weak Segment Labels
Yaxuan Wang, Hao Cheng, Jing Xiong +6
Detecting anomalies in temporal data has gained significant attention across various real-world applications, aiming to identify unusual events and mitigate potential hazards. In p…
GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network
Weiqi Chen, Zhiqiang Zhou, Qingsong Wen +1
Time series subsequence anomaly detection is an important task in a large variety of real-world applications ranging from health monitoring to AIOps, and is challenging due to the…
Task-oriented Time Series Imputation Evaluation via Generalized Representers
Zhixian Wang, Linxiao Yang, Liang Sun +2
Time series analysis is widely used in many fields such as power energy, economics, and transportation, including different tasks such as forecasting, anomaly detection, classifica…
Evolving Multi-Scale Normalization for Time Series Forecasting under Distribution Shifts
Dalin Qin, Yehui Li, Weiqi Chen +5
Complex distribution shifts are the main obstacle to achieving accurate long-term time series forecasting. Several efforts have been conducted to capture the distribution character…
RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomalies
Hao Cheng, Qingsong Wen, Yang Liu +1
Time series forecasting is an important and forefront task in many real-world applications. However, most of time series forecasting techniques assume that the training data is cle…