提出常数时间复杂度训练方法，解决时序预测中深层特征学习过慢问题。
Learning K-U-Net with constant complexity: An Application to time series forecasting
- 设计新型指数加权随机梯度算法，实现恒定时间复杂度
- 在合成数据上验证，测试集准确率提升且复杂度显著降低
- 适合需要高效训练的深度时序预测场景
深度模型用于时序预测面临时间复杂度高的挑战。尽管现有方法通常保证线性时间复杂度，但我们观察到高阶特征的学习速度比低阶特征慢98.44%。为此，提出一种新的指数加权随机梯度下降算法，旨在使深度学习模型达到常数时间复杂度。理论证明该学习方法的时间复杂度为常数。在合成数据集上对Kernel U-Net（K-U-Net）的评估表明，该方法在显著降低复杂度的同时提升了测试集准确率。
原文摘要 · Abstract (English)
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 are learned 98.44\% slower than low-level features. To address this issue, we introduce a new exponentially weighted stochastic gradient descent algorithm designed to achieve constant time complexity in deep learning models. We prove that the theoretical complexity of this learning method is constant. Evaluation of this method on Kernel U-Net (K-U-Net) on synthetic datasets shows a significant reduction in complexity while improving the accuracy of the test set.
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