面向多变量时间序列缺失值插补的切换稀疏网络挖掘
Mining of Switching Sparse Networks for Missing Value Imputation in Multivariate Time Series
AI总结：
针对多变量时间序列缺失值问题，提出可挖掘切换稀疏网络与时间依赖的MissNet插补方法，线性复杂度，性能优于现有最优算法且结果可解释。
摘要：
多变量时间序列数据存在缺失值问题，这阻碍了许多分析方法的应用。为实现这些缺失值的准确插补，利用序列间关系（即网络）挖掘序列间相关性与利用时间依赖性同等重要，因为一条序列通常与其他序列存在关联。此外，由于网络会随时间变化，挖掘适配时间的恰当网络也十分必要。但在现实场景中，我们通常既无法预先知晓网络结构，也无法预知网络何时发生变化。对此，我们提出一种多变量时间序列缺失值插补方法MissNet，该方法旨在通过状态空间模型利用时间依赖性，并通过切换稀疏网络挖掘序列间相关性。该网络对特征间的条件独立性进行编码，有助于我们直观理解对插补至关重要的关联关系。我们的算法计算复杂度与数据长度呈线性关系，它在发现网络切换的同时，交替进行网络推断并利用推断出的网络填充缺失值。大量实验表明，MissNet在多变量时间序列插补任务上的表现优于现有最优算法，且能提供可解释的结果。
英文摘要：
Multivariate time series data suffer from the problem of missing values, which hinders the application of many analytical methods. To achieve the accurate imputation of these missing values, exploiting inter-correlation by employing the relationships between sequences (i.e., a network) is as important as the use of temporal dependency, since a sequence normally correlates with other sequences. Moreover, exploiting an adequate network depending on time is also necessary since the network varies over time. However, in real-world scenarios, we normally know neither the network structure nor when the network changes beforehand. Here, we propose a missing value imputation method for multivariate time series, namely MissNet, that is designed to exploit temporal dependency with a state-space model and inter-correlation by switching sparse networks. The network encodes conditional independence between features, which helps us understand the important relationships for imputation visually. Our algorithm, which scales linearly with reference to the length of the data, alternatively infers networks and fills in missing values using the networks while discovering the switching of the networks. Extensive experiments demonstrate that MissNet outperforms the state-of-the-art algorithms for multivariate time series imputation and provides interpretable 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。
