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pro vyhledávání: '"SIARRY Patrick"'
Training deep models for time series forecasting is a critical task with an inherent challenge of time complexity. While current methods generally ensure linear time complexity, our observations on temporal redundancy show that high-level features ar
Externí odkaz:
http://arxiv.org/abs/2410.02438
Anomaly detection in time series data is a critical challenge across various domains. Traditional methods typically focus on identifying anomalies in immediate subsequent steps, often underestimating the significance of temporal dynamics such as dela
Externí odkaz:
http://arxiv.org/abs/2408.04377
Forecasting multivariate time series is a computationally intensive task challenged by extreme or redundant samples. Recent resampling methods aim to increase training efficiency by reweighting samples based on their running losses. However, these me
Externí odkaz:
http://arxiv.org/abs/2406.13871
Autor:
Wang, Baiyi, Zhang, Zipeng, Siarry, Patrick, Liu, Xinhua, Królczyk, Grzegorz, Hua, Dezheng, Brumercik, Frantisek, Li, Zhixiong
In order to alleviate the main shortcomings of the AVOA, a nonlinear African vulture optimization algorithm combining Henon chaotic mapping theory and reverse learning competition strategy (HWEAVOA) is proposed. Firstly, the Henon chaotic mapping the
Externí odkaz:
http://arxiv.org/abs/2403.15505
Time series forecasting task predicts future trends based on historical information. Transformer-based U-Net architectures, despite their success in medical image segmentation, have limitations in both expressiveness and computation efficiency in tim
Externí odkaz:
http://arxiv.org/abs/2401.01479
Publikováno v:
In Expert Systems With Applications 15 January 2025 260
