发表机构
Imperial College London（伦敦帝国理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出谱对齐潜在流时间序列生成器，通过傅里叶、小波及签名变换的微调损失改善谱失配，在真实基准上验证了其生成真实性与效率优势。
AI 中文摘要
潜在流模型已被证明是时间序列生成中一种可靠且经济有效的方法。然而，潜在压缩会引入不希望的伪影，例如与底层数据集相关的谱失配，从而阻碍其作为训练替代数据的用途。在本文中，我们提出了一种谱对齐的潜在流时间序列生成器，其中用于流匹配的潜在空间经过训练，以保留对合成样本适用性至关重要的动态特性。我们发现，引入基于典型信号表示（如傅里叶变换、小波变换和签名变换）的微调损失有助于克服这些问题。这些变换的可解释性使我们能够确保合成信号在相关特征（如平滑度或目标谱内容）方面与真实信号对齐，而不是仅依赖逐点重建损失。我们将所提出的对齐模型与基础潜在流模型以及最先进的方法在真实世界的长程单变量和多变量基准数据集上进行了比较。我们的定量结果验证了所提方法在反映信号真实性和计算效率的指标上的优越性，同时在与训练集局部结构对齐方面也表现出色。
英文摘要
Latent flow models have proven to be a reliable and cost-effective method for time series generation. However, the latent compression induces unwanted artefacts, such as a spectral mismatch with respect to the underlying dataset, thus hindering their use as training surrogates. In this article, we propose a spectrally-aligned latent-flow time series generator, where the latent space for flow matching is trained to preserve dynamical properties that are relevant for the suitability of synthetic samples. We find that incorporating fine-tuning losses based on canonical signal representations such as the Fourier, wavelet and signature transforms helps overcome these issues. The interpretability of these transformations allows us to ensure that the synthetic signals are aligned with the true ones in terms of relevant features, such as smoothness or targeted spectral content, as opposed to relying on pointwise reconstruction losses only. We compare the proposed aligned models against a base latent-flow model and the state of the art over real-world long-range univariate and multivariate benchmark datasets. Our quantitative results validate the superiority of the proposed method in terms of its performance on metrics reflecting signal realness and computational efficiency, while being aligned to the training set with respect to its local structure.
