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20182024
most citedDCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection

325 citations · 732 across the 24 of their papers we have counts for

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Showing 2023Show all

9 papers · 1 filter

cs.LG2023

Interactive Generalized Additive Model and Its Applications in Electric Load Forecasting

Linxiao Yang, Rui Ren, Xinyue Gu +1

Electric load forecasting is an indispensable component of electric power system planning and management. Inaccurate load forecasting may lead to the threat of outages or a waste o…

cs.LG2023★ 21 cited

OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling

Yi-Fan Zhang, Qingsong Wen, Xue Wang +6

Online updating of time series forecasting models aims to address the concept drifting problem by efficiently updating forecasting models based on streaming data. Many algorithms a…

cs.LG2023★ 2 cited

BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition

Shikai Fang, Qingsong Wen, Yingtao Luo +2

In real-world scenarios like traffic and energy, massive time-series data with missing values and noises are widely observed, even sampled irregularly. While many imputation method…

cs.LG2023

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…

cs.LG2023★ 325 cited

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…

cs.LG2023★ 4 cited

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…