Time Series Anomaly Detection Method Based on Transformer and GAN
In the realm of time-series data analysis, anomaly detection stands as one of the most matured applications. It finds extensive use in real-world sectors such as quantitative trading, network security detection, autonomous driving, and routine maintenance of large industrial equipment. With the burgeoning complexity of business combinations and the volume of time-series data, traditional manual methods and simplistic algorithmic approaches fall short in identifying anomalies. Addressing this, improvements have been made to existing detection methodologies, culminating in the proposition of a time-series anomaly detection model grounded in both Transformer and Generative Adversarial Network ( GAN ) architectures. The refined Transformer is adept at extracting spatial features from time series, and it employs an anomaly detection algorithm based on anomaly scores, in conjunction with adversarial training, to achieve both stability and accuracy. The model is trained in a self-supervised manner, circumventing the tediousness of manual anomaly labeling and reducing the dataset reliance for supervised model training. Empirical validation showcases that the proposed Transformer-based time-series anomaly detection model stands on par in accuracy with current state-of-the-art Transformer-based models and outperforms supervised training on large multivariate time-series datasets and traditional anomaly detection techniques. Hence, this model harbors significant potential for practical applications.
deep learninganomaly detectionTransformerGANmultivariate time series
万方数据
维普
