DeepBooTS: 基于双流残差提升的抗漂移时间序列预测
DeepBooTS: Dual-Stream Residual Boosting for Drift-Resilient Time-Series Forecasting
AI总结：
DeepBooTS通过双流残差提升方法，提升时间序列预测的抗漂移鲁棒性，实验表明在多种数据集上性能提升显著。
摘要：
时间序列（TS）表现出显著的非平稳性。因此，尽管实例归一化被广泛应用于预测方法中，但大多数预测方法在概念漂移下的鲁棒性仍受到损害。我们通过从偏差-方差的角度分析概念漂移，并证明加权集成可以减少方差而不增加偏差。这些见解促使我们提出DeepBooTS，一种新颖的端到端双流残差减少提升方法，逐步重建内在信号。在我们的设计中，每个深度模型的块都成为集成学习者，其辅助输出分支形成一条高速公路到最终预测。块级输出纠正了前一区块的残差，导致学习驱动的输入和目标的分解。该方法在增强通用性和可解释性的同时，显著提高了对概念漂移的鲁棒性。广泛的实验，包括在大规模数据集上的实验，表明所提出的方法在各种数据集上平均性能提高了15.8%，建立了时间序列预测的新基准。
英文摘要：
Time-Series (TS) exhibits pronounced non-stationarity. Consequently, most forecasting methods display compromised robustness to concept drift, despite the prevalent application of instance normalization. We tackle this challenge by first analysing concept drift through a bias-variance lens and proving that weighted ensemble reduces variance without increasing bias. These insights motivate DeepBooTS, a novel end-to-end dual-stream residual-decreasing boosting method that progressively reconstructs the intrinsic signal. In our design, each block of a deep model becomes an ensemble of learners with an auxiliary output branch forming a highway to the final prediction. The block-wise outputs correct the residuals of previous blocks, leading to a learning-driven decomposition of both inputs and targets. This method enhances versatility and interpretability while substantially improving robustness to concept drift. Extensive experiments, including those on large-scale datasets, show that the proposed method outperforms existing methods by a large margin, yielding an average performance improvement of 15.8% across various datasets, establishing a new benchmark for TS forecasting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
