TIMBA：基于双向Mamba模块与扩散模型的时间序列插补
TIMBA: Time series Imputation with Bi-directional Mamba Blocks and Diffusion models
AI总结：
针对多变量时间序列插补问题，提出融合双向Mamba模块、图神经网络与扩散模型的TIMBA方法，在多场景和真实数据集上表现优异，并开源代码。
摘要：
多变量时间序列的插补问题广泛应用于从临床医疗到多传感器系统的众多领域。最初，循环神经网络（RNN）被用于该任务，但由于其存在误差累积问题，研究人员转而采用Transformer，利用注意力机制缓解这些问题。与此同时，扩散模型在捕捉原始分布方面展现出的优异成果使其成为当前研究前沿，且常与Transformer结合使用。本文提出用更适合时序数据建模的状态空间模型（SSM）替代面向时间的Transformer。具体而言，我们采用最新的SSM变体S6，该变体融入了类注意力机制。通过将S6嵌入Mamba模块，我们开发了一个融合状态空间模型、图神经网络以及面向节点的Transformer的模型，以实现更优的时空表征。基于这些该领域此前未被探索的架构改进，我们提出了双向Mamba模块与扩散模型时间序列插补方法（TIMBA）。在多种缺失值场景及三个真实数据集上，TIMBA在几乎所有基准测试场景中均取得了最优性能，在其余场景中表现也具有竞争力。我们还评估了模型性能随缺失值数量变化的规律，并分析了其在下游任务上的表现。此外，我们提供了原始代码以复现实验结果。
英文摘要：
The problem of imputing multivariate time series spans a wide range of fields, from clinical healthcare to multi-sensor systems. Initially, Recurrent Neural Networks (RNNs) were employed for this task; however, their error accumulation issues led to the adoption of Transformers, leveraging attention mechanisms to mitigate these problems. Concurrently, the promising results of diffusion models in capturing original distributions have positioned them at the forefront of current research, often in conjunction with Transformers. In this paper, we propose replacing time-oriented Transformers with State-Space Models (SSM), which are better suited for temporal data modeling. Specifically, we utilize the latest SSM variant, S6, which incorporates attention-like mechanisms. By embedding S6 within Mamba blocks, we develop a model that integrates SSM, Graph Neural Networks, and node-oriented Transformers to achieve enhanced spatiotemporal representations. Implementing these architectural modifications, previously unexplored in this field, we present Time series Imputation with Bi-directional mamba blocks and diffusion models (TIMBA). TIMBA achieves superior performance in almost all benchmark scenarios and performs comparably in others across a diverse range of missing value situations and three real-world datasets. We also evaluate how the performance of our model varies with different amounts of missing values and analyse its performance on downstream tasks. In addition, we provide the original code to replicate the results.
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。
