Anomaly detection in temporal data from sensors under aviation scenarios is a practical but challenging task: 1) long temporal data is difficult to extract contextual information with temporal correlation; 2) the anomalous data are rare in time series, causing normal/abnormal imbalance in anomaly detection, making the detector classification degenerate or even fail. To remedy the aforementioned problems, we propose a Graphical Temporal Data Analysis (GTDA) framework. It consists three modules, named Series-to-Image (S2I), Cluster-based Resampling Approach using Euclidean Distance (CRD) and Variance-Based Loss (VBL). Specifically, for better extracts global information in temporal data from sensors, S2I converts the data to curve images to demonstrate abnormalities in data changes. CRD and VBL balance the classification to mitigate the unequal distribution of classes. CRD extracts minority samples with similar features to majority samples by clustering and over-samples them. And VBL fine-tunes the decision boundary by balancing the fitting degree of the network to each class. Ablation experiments on the Flights dataset indicate the effectiveness of CRD and VBL on precision and recall, respectively. Extensive experiments demonstrate the synergistic advantages of CRD and VBL on F1-score on Flights and three other temporal datasets.

翻译：航空场景下传感器时序数据的异常检测是一项实际但具有挑战性的任务：1) 长时序数据难以通过时间相关性提取上下文信息；2) 时间序列中异常数据稀少，导致异常检测中正负样本不平衡，使检测器分类性能退化甚至失效。针对上述问题，我们提出了一种图形化时序数据分析（GTDA）框架。该框架包含三个模块，分别命名为序列转图像（S2I）、基于欧氏距离的聚类重采样方法（CRD）和基于方差的损失函数（VBL）。具体而言，为更好地提取传感器时序数据中的全局信息，S2I将数据转换为曲线图像以直观展示数据变化中的异常特征。CRD和VBL通过平衡分类过程缓解类分布不均问题：CRD通过聚类提取与多数类样本特征相似的少数类样本并进行过采样；VBL则通过平衡网络对各类别的拟合程度来微调决策边界。在Flights数据集上的消融实验表明CRD和VBL分别对精确率和召回率具有有效性。大量实验验证了CRD与VBL在Flights及其他三个时序数据集上对F1分数的协同优势。