标题： OccamVTS：将视觉模型压缩至1%参数用于时间序列预测 显示英文标题

标题： OccamVTS: Distilling Vision Models to 1% Parameters for Time Series Forecasting

摘要： 时间序列预测对于多种应用至关重要，最近的方法利用大型视觉模型（LVMs）通过视觉表示来捕捉时间模式。 我们发现，尽管视觉模型提高了预测性能，但其99%的参数对于时间序列任务是不必要的。 通过跨模态分析，我们发现时间序列与低级纹理特征一致，但不与高级语义一致，这可能会影响预测准确性。 我们提出了OccamVTS，这是一种知识蒸馏框架，将LVMs中的仅1%的预测信息提取到轻量级网络中。 使用预训练的LVMs作为优势教师，OccamVTS采用金字塔风格的特征对齐，结合相关性和特征蒸馏，以传递有益模式同时过滤语义噪声。 出乎意料的是，这种激进的参数减少通过消除对无关视觉特征的过拟合而提高了准确性，同时保留了必要的时间模式。 在多个基准数据集上的广泛实验表明，OccamVTS仅使用原始参数的1%就能持续达到最先进的性能，特别是在少样本和零样本场景中表现尤为出色。 显示英文摘要

摘要： Time series forecasting is fundamental to diverse applications, with recent approaches leverage large vision models (LVMs) to capture temporal patterns through visual representations. We reveal that while vision models enhance forecasting performance, 99% of their parameters are unnecessary for time series tasks. Through cross-modal analysis, we find that time series align with low-level textural features but not high-level semantics, which can impair forecasting accuracy. We propose OccamVTS, a knowledge distillation framework that extracts only the essential 1% of predictive information from LVMs into lightweight networks. Using pre-trained LVMs as privileged teachers, OccamVTS employs pyramid-style feature alignment combined with correlation and feature distillation to transfer beneficial patterns while filtering out semantic noise. Counterintuitively, this aggressive parameter reduction improves accuracy by eliminating overfitting to irrelevant visual features while preserving essential temporal patterns. Extensive experiments across multiple benchmark datasets demonstrate that OccamVTS consistently achieves state-of-the-art performance with only 1% of the original parameters, particularly excelling in few-shot and zero-shot scenarios.