551 citations · 1.3k across the 11 of their papers we have counts for
11 papers
SaDI: A Self-adaptive Decomposed Interpretable Framework for Electric Load Forecasting under Extreme Events
Hengbo Liu, Ziqing Ma, Linxiao Yang +5
Accurate prediction of electric load is crucial in power grid planning and management. In this paper, we solve the electric load forecasting problem under extreme events such as sc…
DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection
Yiyuan Yang, Chaoli Zhang, Tian Zhou +2
Time series anomaly detection is critical for a wide range of applications. It aims to identify deviant samples from the normal sample distribution in time series. The most fundame…
GCformer: An Efficient Framework for Accurate and Scalable Long-Term Multivariate Time Series Forecasting
YanJun Zhao, Ziqing Ma, Tian Zhou +3
Transformer-based models have emerged as promising tools for time series forecasting. However, these model cannot make accurate prediction for long input time series. On the one ha…
CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting
Wang Xue, Tian Zhou, Qingsong Wen +3
Recent studies have demonstrated the great power of Transformer models for time series forecasting. One of the key elements that lead to the transformer's success is the channel-in…
Towards Expressive Spectral-Temporal Graph Neural Networks for Time Series Forecasting
Ming Jin, Guangsi Shi, Yuan-Fang Li +7
Time series forecasting has remained a focal point due to its vital applications in sectors such as energy management and transportation planning. Spectral-temporal graph neural ne…
One Fits All:Power General Time Series Analysis by Pretrained LM
Tian Zhou, PeiSong Niu, Xue Wang +2
Although we have witnessed great success of pre-trained models in natural language processing (NLP) and computer vision (CV), limited progress has been made for general time series…
TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis
Chaoli Zhang, Tian Zhou, Qingsong Wen +1
Time series anomaly detection is a challenging problem due to the complex temporal dependencies and the limited label data. Although some algorithms including both traditional and…
TreeDRNet:A Robust Deep Model for Long Term Time Series Forecasting
Tian Zhou, Jianqing Zhu, Xue Wang +4
Various deep learning models, especially some latest Transformer-based approaches, have greatly improved the state-of-art performance for long-term time series forecasting.However,…
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting
Tian Zhou, Ziqing Ma, Xue wang +5
Recent studies have shown that deep learning models such as RNNs and Transformers have brought significant performance gains for long-term forecasting of time series because they e…
Transformers in Time Series: A Survey
Qingsong Wen, Tian Zhou, Chaoli Zhang +4
Transformers have achieved superior performances in many tasks in natural language processing and computer vision, which also triggered great interest in the time series community.…
FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting
Tian Zhou, Ziqing Ma, Qingsong Wen +3
Although Transformer-based methods have significantly improved state-of-the-art results for long-term series forecasting, they are not only computationally expensive but more impor…