Application of a novel data-driven framework in anomaly detection of industrial data

In recent years, deep learning algorithms represented by the Transformer model have gained widespread recognition in the application of time series anomaly detection. This paper proposes a novel data-driven Saxformer improvement framework based on the Boosting algorithm for multidimensional time series anomaly detection. This framework enhances the Transformer model’s ability to capture local feature information and improves both the robustness to noise and the flexibility in practical applications. The method of improving the Transformer output layer using Boosting classifiers is designed for fine control. By outputting text letter by letter, the method aims to balance the model’s creativity and conservatism, suppressing significant fluctuations in predictions and thereby enhancing detection accuracy. Experimental results demonstrate that the proposed model outperforms existing mainstream algorithms on multiple evaluation metrics. The F1 score, Precision, and Recall reach 0.882, 0.946, and 0.825, respectively. Compared with the best-performing baseline model, the F1 score is enhanced by 1.4%, demonstrating high accuracy in time series anomaly detection tasks.