文章摘要
杨亚莉1,李智伟1,钟卫军2.基于二向注意力循环神经网络的PM2.5浓度预测[J].空军工程大学学报:自然科学版,2020,21(6):101-106
基于二向注意力循环神经网络的PM2.5浓度预测
Prediction of PM2.5 Concentration Based on a Two-Direction Attention Based Recurrent Neural Network
  
DOI:
中文关键词: PM2.5  时间序列预测  深度学习  循环神经网络  注意力机制
英文关键词: PM2.5  time series prediction  deep learning  recurrent neural network  attention mechanism
基金项目:国家自然科学基金(61801518);空军工程大学基础部研究生创新基金
作者单位
杨亚莉1,李智伟1,钟卫军2 1.空军工程大学基础部 西安 710051 2.宇航动力学国家重点实验室 西安 710043 
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中文摘要:
      针对PM2.5浓度预测模型效果不稳定、泛化能力差的问题,以循环神经网络和注意力机制为基础,提出了二向注意力循环神经网络(TDA RNN)。首先,TDA-RNN模型通过注意力机制获取输入数据的时序注意力和类别注意力,并将其进行融合;然后通过特征编码器对融合后的数据进行编码,获得中间特征;最后将中间特征与PM2.5浓度的历史信息融合,并通过特征解码器获取预测值。对北京地区的PM2.5浓度进行了预测。结果表明,相比前向型神经网络、长短期记忆神经网络、门控循环单元模型和滑动平均模型,TDA-RNN模型预测精度更高;在抗干扰测试中,当输入数据存在无关因素时,TDA RNN模型的预测精度出现轻微下降,但仍高于其他模型。该二向注意力循环神经网络特征提取能力强,预测精度高,同时可适用于其他场景的多变量时间序列预测。
英文摘要:
      Aimed at the problems that PM2.5 concentration prediction model is unstable in efficiency and poor in generalization ability, a Two Direction Attention based Recurrent Neural Network (TDA-RNN) is proposed based on the cyclic neural network and attention mechanism. Firstly, the temporal attention and the category attention in inputting data through attention mechanism are obtained in making them fused by TDA-RNN model, and then the fused data are encoded to obtain intermediate features through feature encoder. Finally, the intermediate features are fused with historical information of PM2.5 concentration, and the predicted values are obtained by feature decoder. The PM2.5 concentration in Beijing is predicted by several models. The results show that the prediction accuracy of TDA-RNN is higher than that of the Back Propagation Neural Network, the Long Short Term Memory, the Gate Recurrent Unit and the Moving Average model. In the anti jamming test, while the input data having noise factors, the prediction accuracy of TDA-RNN decreases slightly, but still higher than that of other models. The TDA-RNN proposed in the paper is strong in feature extraction ability and high in prediction accuracy.And this can also be applied to multivariate time series prediction in other application scenarios.
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