A Novel Robust HL-Based Transformer Approach for Predicting Electrical Fire Risks

Accurate prediction of electrical fire risk is important for early warning, but real-world monitoring data are often affected by sensor noise, transient anomalies, and non-Gaussian interference. This study proposes an HL-Transformer that incorporates an HL-Pooling layer based on the Hodges–Lehmann estimator into the Transformer feature aggregation process. The HL-Pooling layer replaces conventional mean- or max-based pooling by using the median of pairwise averages, aiming to suppress abnormal perturbations while preserving temporal information. Experiments were conducted on a real-world electrical fire monitoring dataset and the public ETTh1 dataset, with additional robustness tests under different outlier ratios and intensities. The results show that, within the same Transformer backbone, HL-Transformer reduced the MSE by 75.4% compared with the Max-Pooling variant and achieved an R2 of 0.879 on the electrical fire risk prediction task. Under injected outliers, the HL-Pooling layer showed more stable error trends, and its transfer to TCN, CNN-LSTM, and 1D-CNN models further improved predictive performance. These findings indicate that HL-Pooling is a robust and portable alternative to conventional pooling for time-series forecasting in noisy monitoring environments.