动态调整学习强度，让模型自动忽略噪声数据。
DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting
- 根据数据噪声程度自适应调整丢弃率，动态控制学习强度。
- 在多种噪声环境下显著提升主流模型性能，无额外参数开销。
- 适合处理真实世界含噪时间序列，无需修改模型结构。
深度时间序列模型在真实场景中易受噪声干扰。现有鲁棒性方法或剔除数据，或依赖昂贵的先验量化，难以兼顾效果与效率。本文提出 DropoutTS，一种模型无关的插件式方法，将学习重点从“学什么”转向“学多少”。通过谱稀疏性分析重构残差，高效量化实例级噪声，并基于噪声水平动态生成自适应丢弃率：抑制异常波动的同时保留细微特征。在多种噪声场景与公开基准上的实验表明，DropoutTS可持续提升主流模型表现，实现先进鲁棒性，且几乎不增加参数量，无需修改模型架构。代码已开源。
原文摘要 · Abstract (English)
Deep time series models are vulnerable to noisy data ubiquitous in real-world applications. Existing robustness strategies either prune data or rely on costly prior quantification, failing to balance effectiveness and efficiency. In this paper, we introduce DropoutTS, a model-agnostic plugin that shifts the paradigm from "what" to learn to "how much" to learn. DropoutTS employs a Sample-Adaptive Dropout mechanism: leveraging spectral sparsity to efficiently quantify instance-level noise via reconstruction residuals, it dynamically calibrates model learning capacity by mapping noise to adaptive dropout rates - selectively suppressing spurious fluctuations while preserving fine-grained fidelity. Extensive experiments across diverse noise regimes and open benchmarks show DropoutTS consistently boosts superior backbones' performance, delivering advanced robustness with negligible parameter overhead and no architectural modifications. Our code is available at https://github.com/CityMind-Lab/DropoutTS.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。