面向含缺失值的多元时间序列异常检测的图时空过程
Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values
AI总结：
本文提出GST-Pro框架，通过神经受控微分方程构建图时空过程，并结合基于分布的异常评分机制，有效解决含缺失值的不规则采样多元时间序列异常检测问题，性能优于现有方法。
摘要：
多元时间序列数据中的异常检测对于智能电网、交通流预测和工业过程控制等多种实际应用至关重要。然而，现实世界的时间序列数据通常结构不佳，给现有方法带来了重大挑战：（1）多元时间序列数据在变量维度和时间维度上存在缺失值，阻碍了对交织的时空依赖关系进行有效建模，导致模型训练过程中重要模式被忽略；（2）对不规则采样观测进行异常评分的研究较少，使得现有检测器难以用于没有完整观测值的多元序列。在这项工作中，我们引入了一个名为GST-Pro的新框架，该框架利用图时空过程和异常评分器来解决上述在不规则采样多元时间序列上检测异常的挑战。我们的方法包含两个主要组成部分。首先，我们提出了一个基于神经受控微分方程的图时空过程。即使数据包含缺失值，该过程也能从空间和时间两个视角对多元时间序列进行有效建模。其次，我们提出了一种新颖的基于分布的异常评分机制，减轻了对完整均匀观测的依赖。通过分析图时空过程的预测结果，我们的方法使异常能够被容易地检测出来。我们的实验结果表明，GST-Pro方法能够有效检测时间序列数据中的异常，并且无论数据中是否存在缺失值，其性能均优于最先进的方法。我们的代码可在以下地址获取：https://github.com/huankoh/GST-Pro。
英文摘要：
The detection of anomalies in multivariate time series data is crucial for various practical applications, including smart power grids, traffic flow forecasting, and industrial process control. However, real-world time series data is usually not well-structured, posting significant challenges to existing approaches: (1) The existence of missing values in multivariate time series data along variable and time dimensions hinders the effective modeling of interwoven spatial and temporal dependencies, resulting in important patterns being overlooked during model training; (2) Anomaly scoring with irregularly-sampled observations is less explored, making it difficult to use existing detectors for multivariate series without fully-observed values. In this work, we introduce a novel framework called GST-Pro, which utilizes a graph spatiotemporal process and anomaly scorer to tackle the aforementioned challenges in detecting anomalies on irregularly-sampled multivariate time series. Our approach comprises two main components. First, we propose a graph spatiotemporal process based on neural controlled differential equations. This process enables effective modeling of multivariate time series from both spatial and temporal perspectives, even when the data contains missing values. Second, we present a novel distribution-based anomaly scoring mechanism that alleviates the reliance on complete uniform observations. By analyzing the predictions of the graph spatiotemporal process, our approach allows anomalies to be easily detected. Our experimental results show that the GST-Pro method can effectively detect anomalies in time series data and outperforms state-of-the-art methods, regardless of whether there are missing values present in the data. Our code is available: https://github.com/huankoh/GST-Pro.
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。
