用于PM$_{2.5}$时间序列预测的频率感知注意力-LSTM
Frequency-Aware Attention-LSTM for PM$_{2.5}$ Time Series Forecasting
AI总结：
针对PM$_{2.5}$浓度预测问题，提出融合频域分解（STL+FFT）、堆叠LSTM与多头注意力的FALNet模型，在真实数据集上的MAE等指标优于传统模型，可有效捕捉污染波动，为环境决策提供支持。
摘要：
为提升PM$_{2.5}$浓度预测的准确性与鲁棒性，本文提出FALNet——一种融合频域分解、时间建模与注意力细化的频率感知LSTM网络。该模型首先应用STL和FFT提取趋势、季节及去噪残差分量，有效滤除高频噪声；然后将滤波后的残差输入堆叠LSTM捕捉长期依赖，再通过多头注意力机制动态聚焦关键时间步。基于真实城市空气质量数据集的实验表明，FALNet在MAE、RMSE和$R^2$等标准指标上持续优于传统模型，在污染峰值的剧烈波动和非平稳条件下表现出强适应性，验证了其在实时空气污染预测、环境风险评估及决策支持中的有效性与泛化性。
英文摘要：
To enhance the accuracy and robustness of PM$_{2.5}$ concentration forecasting, this paper introduces FALNet, a Frequency-Aware LSTM Network that integrates frequency-domain decomposition, temporal modeling, and attention-based refinement. The model first applies STL and FFT to extract trend, seasonal, and denoised residual components, effectively filtering out high-frequency noise. The filtered residuals are then fed into a stacked LSTM to capture long-term dependencies, followed by a multi-head attention mechanism that dynamically focuses on key time steps. Experiments conducted on real-world urban air quality datasets demonstrate that FALNet consistently outperforms conventional models across standard metrics such as MAE, RMSE, and $R^2$. The model shows strong adaptability in capturing sharp fluctuations during pollution peaks and non-stationary conditions. These results validate the effectiveness and generalizability of FALNet for real-time air pollution prediction, environmental risk assessment, and decision-making support.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
