基于深度学习的服装流行趋势预测研究  

Research on Fashion Trend Forecasting Based on Deep Learning

作　　者：张春发 赵书 李博文 ZHANG Chunfa;ZHAO Shu;LI Bowen(School of Electronics and Information,Xi'an Polytechnic University,Xi'an Shaanxi 710000,China)

机构地区：[1]西安工程大学电子信息学院,陕西西安710000

出　　处：《信息与电脑》2023年第4期208-211,共4页Information & Computer

摘　　要：现有的服装流行趋势预测方法多采用传统的时间序列预测方法,数据来源多为电商网站的销售数据,在预测精度方面具有较大的误差。为解决此问题,文章提出了一种基于卷积神经网络、双向长短时记忆(Bi-directional Long Short-Term Memory,BiLSTM)网络和注意力机制的服装流行趋势预测模型。实验结果表明,本文提出的模型在服装流行趋势预测中优于传统的时间序列预测模型和简单的深度神经网络模型。The existing fashion trend prediction methods mostly use traditional time series prediction methods,and the data sources are mostly sales data from e-commerce websites,which has significant errors in prediction accuracy.This paper proposes a clothing trend prediction model based on convolutional neural network,bidirectional long-term and short-term memory network,and attention mechanism.The experimental results show that the model proposed in this paper is superior to traditional time series prediction models and simple deep neural network models in fashion trend prediction.

关 键 词：长短时记忆(LSTM)网络 双向长短时记忆(BiLSTM)网络 时间序列预测 流行趋势预测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]