用频域方法压缩时间序列数据集，提升训练效率与泛化能力
Harmonic Dataset Distillation for Time Series Forecasting
- 通过FFT将时序分解为正弦基，全局更新保持时序依赖
- 在多个模型架构上实现接近原始数据的预测性能
- 适合大规模真实场景的时间序列建模需求
现代时间序列预测面临海量数据带来的巨大计算与存储开销。数据集蒸馏（DD）作为一种将大规模数据集压缩为小而紧凑数据集以保持训练效果的方法，展现出广阔前景。然而，传统DD方法未针对时间序列特性设计，存在结构过拟合和可扩展性差的问题。为此，本文提出面向时间序列预测的谐波数据集蒸馏方法（HDT）。HDT通过快速傅里叶变换（FFT）将时间序列分解为正弦基，并利用谐波匹配对齐核心周期结构。由于该过程在频域中进行，蒸馏期间的所有更新均全局施加，不破坏时间序列的时序依赖关系。大量实验表明，HDT具备出色的跨架构泛化能力和可扩展性，验证了其在大规模真实应用中的实用性。
原文摘要 · Abstract (English)
Time Series forecasting (TSF) in the modern era faces significant computational and storage cost challenges due to the massive scale of real-world data. Dataset Distillation (DD), a paradigm that synthesizes a small, compact dataset to achieve training performance comparable to that of the original dataset, has emerged as a promising solution. However, conventional DD methods are not tailored for time series and suffer from architectural overfitting and limited scalability. To address these issues, we propose Harmonic Dataset Distillation for Time Series Forecasting (HDT). HDT decomposes the time series into its sinusoidal basis through the FFT and aligns the core periodic structure by Harmonic Matching. Since this process operates in the frequency domain, all updates during distillation are applied globally without disrupting temporal dependencies of time series. Extensive experiments demonstrate that HDT achieves strong cross-architecture generalization and scalability, validating its practicality for large-scale, real-world applications.
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。