这个双编码器迁移框架挺有意思：美国数据预训练、台湾数据适配，PM2.5预测MSE比旧方法降了7%，消融实验也扎实。
针对目标域观测数据有限且源域与目标域统计特性不同的问题，提出一种移位感知双编码器迁移框架。源编码器使用美国10个监测站的逐时观测数据预训练，再用台湾77个站点两年的逐时数据按时间顺序划分训练集和验证集进行适配与评估。在四个基线中，冻结源编码器的双编码器模型达到最佳性能：MSE=21.8960, MAE=3.1597, R^2=0.8725，相比TL-v1的MSE降低约7.1%，相比TL-v2降低约4.1%。消融实验显示移除台湾特定分支导致性能下降最明显，而让源编码器在目标监督下适应得到最优结果（MSE=21.6575, MAE=3.1383, R^2=0.8739）。SHAP分析表明预测主要由近期PM2.5观测和与污染物输送扩散相关的气象变量驱动。
Shift Aware Transfer Learning with Adaptive Dual-Encoder Fusion for PM Forecasting in Data-Limited Environments
Short-horizon forecasting of fine particulate matter (PM2.5) remains difficult when observations from the target domain are limited and the statistical properties of the source and target domains differ. In these settings, models trained only on local data may not capture complex temporal dynamics, while direct transfer learning can result in negative transfer. This study develops a shift-aware dual-encoder transfer framework that combines source-domain knowledge with target-specific representation learning. The source encoder was pretrained using hourly observations from 10 U.S. monitoring locations. The framework was then adapted and evaluated using two years of hourly observations from 77 stations in Taiwan under a chronological train-validation-test protocol. Among the four principal baselines, the frozen-source dual-encoder model achieved the best performance, with MSE = 21.8960, MAE = 3.1597, and R^2 = 0.8725. This corresponds to an MSE reduction of approximately 7.1% relative to TL-v1 and 4.1% relative to TL-v2. The ablation analysis showed that removing the Taiwan-specific branch caused the largest decline in performance. Allowing the source encoder to adapt produced the best overall result, with MSE = 21.6575, MAE = 3.1383, and R^2 = 0.8739. SHAP analysis indicated that predictions were driven mainly by recent PM2.5 observations and meteorological variables related to pollutant transport and dispersion. These results suggest that source-domain knowledge is most effective when target-specific information is preserved and the transferred representation is allowed to adapt under target supervision.