Score-CDM：用于多元时间序列插补的分数加权卷积扩散模型
Score-CDM: Score-Weighted Convolutional Diffusion Model for Multivariate Time Series Imputation
AI总结：
针对多元时间序列插补中局部与全局时间特征难以自适应权衡的问题，提出分数加权卷积扩散模型Score-CDM，通过分数图与自适应感受野模块平衡特征，在三个真实数据集上验证了有效性。
摘要：
多元时间序列（MTS）数据在现实场景中通常是不完整的，对不完整的MTS进行插补对于促进各种时间序列挖掘任务具有实际重要性。近年来，基于扩散模型的MTS插补方法通过利用CNN或注意力机制进行时间特征学习，取得了令人鼓舞的结果。然而，简单地结合CNN和注意力难以自适应地权衡局部和全局时间特征的不同影响。为解决这一问题，我们提出了一种分数加权卷积扩散模型（简称Score-CDM），其主干由分数加权卷积模块（SCM）和自适应感受野模块（ARM）组成。SCM采用分数图来捕获时域中的全局时间特征，而ARM使用频谱到时间窗口块（S2TWB）在频域中对局部时间序列数据进行卷积。得益于快速傅里叶变换的时间卷积特性，ARM可以自适应地改变分数图的感受野，从而有效平衡局部和全局时间特征。我们在三个不同领域的真实MTS数据集上进行了广泛评估，结果验证了所提出的Score-CDM的有效性。
英文摘要：
Multivariant time series (MTS) data are usually incomplete in real scenarios, and imputing the incomplete MTS is practically important to facilitate various time series mining tasks. Recently, diffusion model-based MTS imputation methods have achieved promising results by utilizing CNN or attention mechanisms for temporal feature learning. However, it is hard to adaptively trade off the diverse effects of local and global temporal features by simply combining CNN and attention. To address this issue, we propose a Score-weighted Convolutional Diffusion Model (Score-CDM for short), whose backbone consists of a Score-weighted Convolution Module (SCM) and an Adaptive Reception Module (ARM). SCM adopts a score map to capture the global temporal features in the time domain, while ARM uses a Spectral2Time Window Block (S2TWB) to convolve the local time series data in the spectral domain. Benefiting from the time convolution properties of Fast Fourier Transformation, ARM can adaptively change the receptive field of the score map, and thus effectively balance the local and global temporal features. We conduct extensive evaluations on three real MTS datasets of different domains, and the result verifies the effectiveness of the proposed Score-CDM.
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。
