基于LSTM-DA神经网络的农产品价格指数短期预测模型

摘要/Abstract

摘要： 农产品价格一直是维持社会经济生活安定的重点关注领域,由于农产品预测价格与影响因素之间存在非线性关系,递归神经网络虽然适用于时间序列的预测,但是针对长时间的跨度,其预测效果有限。基于此,根据农产品价格特点,设计了一种LSTM-DA(Long Short-Term Memory-Double Attention,双重注意力机制与长短期记忆网络融合)神经网络模型。它将卷积注意力网络(Convolutional Neural Networks,CNN)、长短期记忆网络(Long Short-Term Memory,LSTM)和注意力机制相结合,针对不同成分的影响因子通过卷积注意力网络进行特征提取,调节其对应的权重并馈送至长短期记忆网络模型中以呈现时间序列的影响,在此基础上,将结果再次送入注意力机制进行权重调节,最终将得到的结果用于农产品价格指数的短期预测。实验前,采用多线程机制从多个农业信息平台中爬取海量的价格、天气等相关数据,在对其进行解析和清洗的基础上,将其存入分布式文件系统(Hadoop Distributed File System,HDFS)中;实验时,采用长短期记忆网络作为基线。实验结果表明,与传统的单一模型相比,此模型不仅可以提升预测精度,而且预测的农产品价格指数可以准确地描述未来一周内蔬菜类产品的整体趋势。

关键词: 长短期记忆网络, 价格预测, 卷积注意力网络, 网络数据爬取, 注意力机制

Abstract: The price of agricultural products has always been the key area for maintaining social and economic life.Due to the non-linear relationship between predicted prices and influencing factors of agricultural products,recurrent neural networks are suitable for time series prediction.However,for long-term span,its prediction effect is limited.According to the price characteristics of agricultural products,a neural network model of LSTM-DA (Long Short-Term Memory-Double Attention) was designed.It combines the convolutional attention network,the Long Short-Term Memory network and the attention mechanism.The attention factors of different components are extracted by the convolutional attention network,and the corresponding weights are adjusted and fed into the Long Short-Term Memory network mo-del.Based on the influence of the time series,the results are sent to the attention mechanism for weight adjustment,and finally the results are used for short-term prediction of agricultural product price index.Before the experiment,the multi-threading mechanism is used to crawl a large number of agricultural information platforms to collect a large amount of price,weather and other related data.Based on the analysis and cleaning,they are stored in a Hadoop Distri-buted File System.In the experiment,the Long Short-Term Memory network is used as the baseline.Compared with the traditional single model,this model can improve the prediction accuracy,and the predicted price index can accurately describe the overall trend of vegetable products in the next week.

Key words: Attention mechanism, Convolutional attention network, Long short-term memory, Network data crawler, Price forecasting

中图分类号:

TP183

贾宁, 郑纯军. 基于LSTM-DA神经网络的农产品价格指数短期预测模型[J]. 计算机科学, 2019, 46(11A): 62-65. https://doi.org/

JIA Ning, ZHENG Chun-jun. Short-term Forecasting Model of Agricultural Product Price Index Based onLSTM-DA Neural Network[J]. Computer Science, 2019, 46(11A): 62-65. https://doi.org/

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