The multivariate time series often contain complex mixed inputs, with complex correlations between them. Detecting change points in multivariate time series is of great importance, which can find anomalies early and reduce losses, yet very challenging as it is affected by many complex factors, i.e., dynamic correlations and external factors. The performance of traditional methods typically scales poorly. In this paper, we propose Finder, a novel approach of change point detection via multivariate fusion attention networks. Our model consists of two key modules. First, in the time series prediction module, we employ multi-level attention networks based on the Transformer and integrate the external factor fusion component, achieving feature extraction and fusion of multivariate data. Secondly, in the change point detection module, a deep learning classifier is used to detect change points, improving efficiency and accuracy. Extensive experiments prove the superiority and effectiveness of Finder on two real-world datasets. Our approach outperforms the state-of-the-art methods by up to 10.50% on the F1 score.

多元时间序列通常包含复杂的混合输入，它们之间具有复杂的相关性。检测多元时间序列中的变化点非常重要，可以及早发现异常并减少损失，但由于受到许多复杂因素的影响，因此非常具有挑战性，即、动态相关性和外部因素。传统方法的性能通常很差。在本文中，我们提出了 Finder，这是一种通过多元融合注意网络检测变化点的新方法。我们的模型由两个关键模块组成。首先，在时间序列预测模块中，我们采用基于Transformer的多级注意力网络，并整合外部因素融合组件，实现多元数据的特征提取和融合。其次，在变化点检测模块中，使用深度学习分类器来检测变化点，提高效率和准确性。大量实验证明了 Finder 在两个真实世界数据集上的优越性和有效性。我们的方法在 F1 分数上比最先进的方法高出 10.50%。