摘要
针对标准编码解码模型(Encoder-Decoder Model, EDM)对于时间序列数据提取能力弱的问题,提出一种融合双向长短时记忆网络(Bi-directional Long Short-Term Memory, Bi-LSTM)和注意力机制(Attention)的编码解码模型.通过Bi-LSTM对输入数据从正反两个方向进行特征提取,基于注意力机制将所得到的特征根据不同时刻分配不同权重,根据解码阶段的不同时刻生成相应背景变量,进而实现对机场客流量的预测.选取上海虹桥机场为例用该算法进行实验仿真,实验结果表明,本文所提方法与RNN、LSTM相比,平均标准误差降低了57.9%以上,为机场客流量预测提供了一种新的思路.
The standard encoder-decoder model has a weak ability to extract time series data. For this reason, this study proposes a novel encoder-decoder model integrating the Bi-directional Long Short-Term Memory(Bi-LSTM) and Attention Mechanism(AM). First, the input data is extracted by Bi-LSTM from both positive and negative directions.Then, different weights are assigned to the obtained features by AM according to different moments, and corresponding background variables are generated according to different moments in the decoding stage. As a result, the airport passenger flow can be predicted. Further, the Shanghai Hongqiao International Airport is taken as an example for the experimental simulation with this algorithm. Experimental results show that compared with Recurrent Neural Network(RNN) and Long Short-Term Memory(LSTM), the proposed method reduces the average standard error by more than57.9%. This study provides a new way for airport passenger flow forecast.
作者
李航
邓颖红
LI Hang;DENG Ying-Hong(School of Economics and Management,Civil Aviation University of China,Tianjin 300300,China)
出处
《计算机系统应用》
2021年第10期307-311,共5页
Computer Systems & Applications
基金
国家科技部项目(2013GXS4B094)。
关键词
编码解码模型
融合双向长短时记忆网络
注意力机制
机场客流量预测
Encoder-Decoder Model(EDM)
Bi-directional Long Short-Term Memory(Bi-LSTM)
Attention Mechanism(AM)
airport passenger flow forecast
