AI 中文总结
针对时间序列基础模型微调中的过拟合和均值预测陷阱，提出SIFT方法，通过语义不变对抗增强和结构保真增强，在10个数据集上显著提升模型性能。
AI 中文摘要
时间序列基础模型（TSFMs）通过在大量时间序列数据集上进行广泛预训练，已实现了显著的零样本性能。然而，由于时间序列数据的低维特性和多样的结构模式，对TSFMs进行朴素微调往往会导致过拟合，并陷入均值预测陷阱。为解决这些挑战，我们提出了SIFT，一种稳健的适应方法，通过在微调过程中保持语义不变性和结构保真来增强时间序列基础模型。我们采用语义不变对抗增强，利用语义频谱分解来划分语义空间，然后在非核心语义子空间内生成扰动，以增强模型对这些扰动的鲁棒性，从而缓解过拟合。我们实现了基于组件的结构保真增强，促进组件级混合，并施加重构目标以提高模型保持结构保真的能力，从而缓解均值预测陷阱。在涵盖10个真实世界数据集的代表性TSFMs上的广泛实验表明，SIFT能够显著提升TSFMs的性能。
英文摘要
Time Series Foundation Models (TSFMs) have achieved remarkable zero-shot performance through extensive pre-training on massive time series datasets. Nevertheless, due to the low-dimensional properties and diverse structural patterns of time series data, performing naive fine-tuning on TSFMs often leads to overfitting and falling into the mean-prediction trap. To address these challenges, we propose SIFT, a robust adaptation method that enhances time series foundation models by preserving Semantic Invariance and structural Fidelity throughout the fine-Tuning process. We employ semantic-invariant adversarial augmentation, which utilizes semantic spectrum decomposition to partition the semantic space and then generates perturbations within the non-core semantic subspace to bolster the model's robustness against these perturbations, mitigating overfitting. We implement a component-based structural fidelity enhancement, which facilitates component-wise mixup and imposes a reconstruction objective to improve the model's ability to preserve structural fidelity, alleviating the mean-prediction trap. Extensive experiments on representative TSFMs covering 10 real-world datasets demonstrate that SIFT can significantly enhance the performance of TSFMs.
