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20192026
most citedOneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling

21 citations · 37 across the 9 of their papers we have counts for

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10 papers · 1 filter

cs.LG2026

Skillful Kilometer-Scale Regional Weather Forecasting via Global and Regional Coupling

Weiqi Chen, Wenwei Wang, Qilong Yuan +5

Data-driven weather models have advanced global medium-range forecasting, yet high-resolution regional prediction remains challenging due to unresolved multiscale interactions betw…

cs.LG2025

TriForecaster: A Mixture of Experts Framework for Multi-Region Electric Load Forecasting with Tri-dimensional Specialization

Zhaoyang Zhu, Zhipeng Zeng, Qiming Chen +4

Electric load forecasting is pivotal for power system operation, planning and decision-making. The rise of smart grids and meters has provided more detailed and high-quality load d…

cs.LG2025

Utilizing Strategic Pre-training to Reduce Overfitting: Baguan -- A Pre-trained Weather Forecasting Model

Peisong Niu, Ziqing Ma, Tian Zhou +4

Weather forecasting has long posed a significant challenge for humanity. While recent AI-based models have surpassed traditional numerical weather prediction (NWP) methods in globa…

cs.LG20241 cited

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…

cs.LG2024

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…

cs.LG2024

Addressing Concept Shift in Online Time Series Forecasting: Detect-then-Adapt

YiFan Zhang, Weiqi Chen, Zhaoyang Zhu +7

Online updating of time series forecasting models aims to tackle the challenge of concept drifting by adjusting forecasting models based on streaming data. While numerous algorithm…