A Prediction-Based Anomaly Detection Method for Traffic Flow Data with Multi-Domain Feature Extraction

The core idea of prediction-based anomaly detection is to identify anomalies by constructing a prediction model and comparing predicted and observed values. However, most existing traffic flow prediction models primarily focus on spatio-temporal features, neglecting comprehensive frequency-domain feature learning. Additionally, anomaly detection accuracy is often limited by insufficient prediction error analysis. To address this limitation, this paper proposes a prediction-based anomaly detection method for traffic flow data with multi-domain feature extraction. The prediction model is built as follows: first, Bidirectional Long Short-Term Memory network (Bi-LSTM) and a Graph Attention Network (GAT) extract temporal and spatial features, respectively. Then, Fast Fourier Transform (FFT) converts time-domain signals into the frequency domain, where Transformer learns magnitude and phase features. Finally, a prediction model is constructed using the extracted time-domain and frequency-domain features. For error analysis, this paper innovatively applies Chebyshev’s inequality to determine the error threshold, identifying anomalies based on whether errors exceed this threshold. Experimental results show that integrating multi-domain features can more comprehensively capture data characteristics and improve model prediction accuracy. In the anomaly detection experiment, it was verified that constructing a high-accuracy prediction model and conducting reasonable error analysis can effectively enable anomaly detection in the data.