基于融合模型动态权值的气温预测  

Temperature prediction based on dynamic weight of fusion model

作　　者：陈岚 文斌[1] 贺南 陈乐 李琪 Chen Lan;Wen Bin;He Nan;Chen Le;Li Qi(School of Communication Engineering,Chengdu University of Information Technology,Chengdu 610225,China;Chengdu Wenjiang District Meteorological Bureau,Chengdu 611130,China)

机构地区：[1]成都信息工程大学通信工程学院,成都610225 [2]成都市温江气象局,成都611130

出　　处：《电子测量技术》2022年第15期68-74,共7页Electronic Measurement Technology

基　　金：四川省教育厅科研项目(2019YFS0490)资助。

摘　　要：气象数据为多元时间序列,为了解决传统气温预测算法预测误差大、时空特征提取不充分的问题,将灰色关联分析、卷积长短时记忆网络和双向长短时记忆网络融合,提出了一种GRA-Conv-BiLSTM气温预测方法。灰色关联分析法解决了传统方法中参数选择困难的问题,然后设定时间窗,结合历史气温作为模型的输入,建立卷积长短时记忆网络和双向长短时记忆网络动态加权融合的预测模型以增强模型的时空特征提取能力,并以四川省某气象站点历史数据作为样本进行实验。结果显示,对于数据量庞大的多元气象时间序列,该模型表现出更强的优越性,能够适应动态非线性变化,预测精度更高。The meteorological data is multi-element time series. In order to solve the problems of large prediction error and insufficient time feature extraction of traditional temperature prediction algorithm, a GRA-Conv-BiLSTM temperature prediction method is proposed by integrating grey correlation analysis, ConvLSTM and BiLSTM together. The grey correlation analysis method is used to solve the problem of difficult parameter selection in the traditional methods, and the time window is set. The grey correlation analysis method solves the problem of difficult parameter selection in traditional methods. The time window is set, combined with the historical temperature as the input of the model, the prediction model of ConvLSTM and BiLSTM dynamic weighted fusion is established to enhance the spatio-temporal feature extraction ability of the model, and the experiment is carried out with the historical data of a meteorological station in Sichuan Province as a sample. The results show that for the multivariate meteorological time series with a large amount of data, the model shows stronger advantages, can adapt to dynamic nonlinear changes and has higher prediction accuracy.

关 键 词：灰色关联度分析 气温预测 时间序列 卷积长短时记忆网络 双向长短时记忆网络 动态加权 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]