DiM-TS:弥合选择性状态空间模型与时间序列生成模型之间的差距
DiM-TS: Bridge the Gap between Selective State Space Models and Time Series for Generative Modeling
AI总结：
DiM-TS通过改进Mamba模型，有效提升时间序列生成的质量与准确性。
摘要：
时间序列数据在众多领域中发挥着关键作用，但面临隐私问题的挑战。最近，通过扩散模型合成数据被视为有前途的解决方案。然而，现有方法仍难以捕捉长期时间依赖性和复杂的通道互相关关系。在本研究中，我们旨在利用一种称为Mamba的状态空间模型的序列建模能力，扩展其在时间序列数据生成中的应用。我们首先分析了状态空间模型的核心局限性，即缺乏对相关时间滞后和通道排列的考虑。基于这一洞察，我们提出了Lag Fusion Mamba和Permutation Scanning Mamba，这些方法增强了模型在去噪过程中识别显著模式的能力。理论分析表明，两种变体都具有与原始Mamba相同的统一矩阵乘法框架，为我们的方法提供了更深入的理解。最后，我们整合了两种变体并引入了扩散Mamba用于时间序列（DiM-TS），这是一种高质量的时间序列生成模型，能够更好地保持时间周期性和通道间相关性。在公共数据集上的综合实验表明，DiM-TS在生成逼真时间序列的同时，能够保持数据的多样性特征。
英文摘要：
Time series data plays a pivotal role in a wide variety of fields but faces challenges related to privacy concerns. Recently, synthesizing data via diffusion models is viewed as a promising solution. However, existing methods still struggle to capture long-range temporal dependencies and complex channel interrelations. In this research, we aim to utilize the sequence modeling capability of a State Space Model called Mamba to extend its applicability to time series data generation. We firstly analyze the core limitations in State Space Model, namely the lack of consideration for correlated temporal lag and channel permutation. Building upon the insight, we propose Lag Fusion Mamba and Permutation Scanning Mamba, which enhance the model's ability to discern significant patterns during the denoising process. Theoretical analysis reveals that both variants exhibit a unified matrix multiplication framework with the original Mamba, offering a deeper understanding of our method. Finally, we integrate two variants and introduce Diffusion Mamba for Time Series (DiM-TS), a high-quality time series generation model that better preserves the temporal periodicity and inter-channel correlations. Comprehensive experiments on public datasets demonstrate the superiority of DiM-TS in generating realistic time series while preserving diverse properties of data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
