most citedContinuous Evolution Pool: Taming Recurring Concept Drift in Online Time Series Forecasting

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cs.LG2026

LVCG: Learning ECG Representations in the Latent Vectorcardiogram Space

Bosong Huang, Panzhen Zhao, Zengxiang Li +5

Electrocardiography (ECG) is a cornerstone of cardiac assessment, making the learning of informative ECG representations fundamental to tasks ranging from disease diagnosis to clin…

cs.LG2026

LeapTS: Rethinking Time Series Forecasting as Adaptive Multi-Horizon Scheduling

Sheng Pan, Ming Jin, Bo Du +1

Time series forecasting serves as an essential tool for many real-world applications, supporting tasks such as resource optimization and decision-making. Despite significant archit…

cs.LG2026

EventTSF: Event-Aware Non-Stationary Time Series Forecasting

Yunfeng Ge, Ming Jin, Yiji Zhao +4

Time series forecasting is vital in diverse sectors such as energy and transportation, where non-stationary dynamics are deeply intertwined with external events in other modalities…

cs.LG2026

The Procrustean Bed of Time Series: The Optimization Bias in Point-wise Loss Functions

Rongyao Cai, Yuxi Wan, Kexin Zhang +6

Intuitively, a more deterministic time series should be easier to forecast. However, point-wise loss functions (e.g., MSE and MAE), serving as differentiable surrogates for the ide…

cs.LG2026

TimeOmni-1: Incentivizing Complex Reasoning with Time Series in Large Language Models

Tong Guan, Zijie Meng, Dianqi Li +7

Recent advances in multimodal time series learning underscore a paradigm shift from analytics centered on basic patterns toward advanced time series understanding and reasoning. Ho…

cs.LG20261 cited

Continuous Evolution Pool: Taming Recurring Concept Drift in Online Time Series Forecasting

Tianxiang Zhan, Ming Jin, Yuanpeng He +3

Recurring concept drift is pervasive in real-world online time series, where the underlying data-generating process repeatedly alternates between a small set of regimes, most notab…