Cross-Modal Deep Metric Learning for Time Series Anomaly Detection
This work addresses time series anomaly detection, which is important for monitoring systems in domains like finance or IoT, but it appears incremental as it builds on existing deep metric learning techniques.
The authors tackled the problem of low sensitivity and high time consumption in time series anomaly detection by proposing a cross-modal deep metric learning method, which achieved high detection accuracy, fast speed, and strong robustness in experiments.
To effectively address the issues of low sensitivity and high time consumption in time series anomaly detection, we propose an anomaly detection method based on cross-modal deep metric learning. A cross-modal deep metric learning feature clustering model is constructed, composed of an input layer, a triplet selection layer, and a loss function computation layer. The squared Euclidean distances between cluster centers are calculated, and a stochastic gradient descent strategy is employed to optimize the model and classify different time series features. The inner product of principal component direction vectors is used as a metric for anomaly measurement. The von Mises-Fisher (vMF) distribution is applied to describe the directional characteristics of time series data, and historical data is used to train and obtain evaluation parameters. By comparing the principal component direction vector of actual time series data with the threshold, anomaly detection is performed. Experimental results demonstrate that the proposed method accurately classifies time series data with different attributes, exhibits high sensitivity to anomalies, and achieves high detection accuracy, fast detection speed, and strong robustness.