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cs.LG2025
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.LG2025
KFS: KAN based adaptive Frequency Selection learning architecture for long term time series forecasting
Changning Wu, Gao Wu, Rongyao Cai +2
Multi-scale decomposition architectures have emerged as predominant methodologies in time series forecasting. However, real-world time series exhibit noise interference across diff…
cs.LG2023★ 18 cited
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects
Kexin Zhang, Qingsong Wen, Chaoli Zhang +8
Self-supervised learning (SSL) has recently achieved impressive performance on various time series tasks. The most prominent advantage of SSL is that it reduces the dependence on l…