用于概率性多元时间序列插补的基于潜空间分数的扩散模型
Latent Space Score-based Diffusion Model for Probabilistic Multivariate Time Series Imputation
AI总结：
针对现有扩散模型忽略低维潜分布、难处理无标签缺失数据的问题，提出LSSDM模型，通过无监督潜空间粗重建结合条件扩散精插补，实现更优的多元时间序列概率插补及不确定性分析。
摘要：
准确的插补对下游任务的可靠性与成功至关重要。近年来，扩散模型在该领域受到广泛关注，但这类模型忽略了从观测数据中得到的低维空间中的潜分布，这限制了扩散模型的生成能力。此外，处理无标签的原始缺失数据问题尤为突出。为解决这些问题，我们提出用于概率性多元时间序列插补的Latent Space Score-Based Diffusion Model（LSSDM，基于潜空间分数的扩散模型）。该无监督学习方法将观测值投影到低维潜空间，在无需知晓缺失数据真实值的情况下重建其粗略值。最后，将重建值输入条件扩散模型，得到时间序列的精确插补值。通过这种方式，LSSDM不仅具备识别潜分布的能力，还能无缝结合扩散模型以获得高保真插补值，并评估数据集的不确定性。实验结果表明，LSSDM实现了更优的插补性能，同时为插补机制提供了更好的可解释性与不确定性分析。代码网址为https://github.com/gorgen2020/LSSDM_imputation。
英文摘要：
Accurate imputation is essential for the reliability and success of downstream tasks. Recently, diffusion models have attracted great attention in this field. However, these models neglect the latent distribution in a lower-dimensional space derived from the observed data, which limits the generative capacity of the diffusion model. Additionally, dealing with the original missing data without labels becomes particularly problematic. To address these issues, we propose the Latent Space Score-Based Diffusion Model (LSSDM) for probabilistic multivariate time series imputation. Observed values are projected onto low-dimensional latent space and coarse values of the missing data are reconstructed without knowing their ground truth values by this unsupervised learning approach. Finally, the reconstructed values are fed into a conditional diffusion model to obtain the precise imputed values of the time series. In this way, LSSDM not only possesses the power to identify the latent distribution but also seamlessly integrates the diffusion model to obtain the high-fidelity imputed values and assess the uncertainty of the dataset. Experimental results demonstrate that LSSDM achieves superior imputation performance while also providing a better explanation and uncertainty analysis of the imputation mechanism. The website of the code is \textit{https://github.com/gorgen2020/LSSDM\_imputation}.
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。
