# Automated Anomaly Detection and Causal Analysis for Civil Aviation Using QAR Data
## 2 Related Works  
QAR data is essentially multidimensional time series data. There are many anomaly detection methods for multidimensional time series, which can be roughly divided into three categories: statistical methods, machine learning methods, and deep learning methods.  
The statistics-based method is the earliest and most mature method for detecting anomalies in multidimensional time series. In past development and application [7,8], it has been found that the statistical method has many shortcomings, such as an inability to consider the order of events and difficulty in determining the boundaries of anomalies. These are fatal drawbacks for anomaly detection in time series.  
In the big data era, with the rapid development of artificial intelligence, machine-learning-based technologies have been applied to solve anomaly detection problems in time series. Compared to deep learning, traditional machine learning algorithms typically have better interpretability in explaining how the model makes predictions or classifications. This is very important for causal analysis of QAR data anomalies.  
[PERSON] et al. [9] proposed a meta-feature-based anomaly detection method (MFAD) for identifying anomalous states in univariate and multivariate time series. The proposed MFAD method not only identifies local abnormal states in the original time series, but also reduces computational complexity. [PERSON] et al. [10] proposed a semi-supervised anomaly detection algorithm using probabilistic labeling (SAD-PL) for unlabeled data, which can ensure stable detection efficiency regardless of changes in the ratio of normal to abnormal data in unlabeled data. [PERSON] et al. [11] proposed a hybrid clustering approach for anomaly detection based on fuzzy logic and evolutionary computation. This method uses a portion of known categories to label data and divides them into a given number of clusters, then attempts to determine whether new instances that may not have been labeled belong to known or new categories, or whether they are anomalies. It can find new classes outside of anomalies while achieving better classification accuracy. Although the above anomaly detection algorithms can effectively detect anomalies through training on large amounts of data, QAR data, as multidimensional time-series data with significant industry characteristics, have the characteristics of heterogeneity and non-periodicity, which make the above methods unable to adapt well. [PERSON] et al. [12] proposed a new model for anomaly detection based on QAR data, which utilizes autoencoders and the DBSCAN algorithm to extract features from QAR data, complementing traditional anomaly detection methods. [PERSON] et al. [13] proposed a three-stage QAR data anomaly detection algorithm considering complex spatial-temporal correlations in QAR data. They also proposed a dual K-means clustering algorithm that can automatically select the hyper-parameter K, which achieved good anomaly detection for QAR data.  
In summary, machine learning-based methods can handle the problems of anomaly detection and causal analysis of time series data. However, these methods used for QAR data mostly focus on data anomalies without considering the requirements of FOQA and cannot produce the causal analysis report automatically. In order to solve the above issues and improve the efficiency of anomaly detection for FOQA, we proposed a machine learning-based method incorporating feature engineering and hyper-parameter optimization techniques.