基于多鉴别器生成对抗网络的时间序列生成模型

陆彦辉; 柳寒; 李航; 朱光旭

doi:10.11959/j.issn.1000-436x.2022205

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基于多鉴别器生成对抗网络的时间序列生成模型

学术论文 | 更新时间：2024-06-05

- 基于多鉴别器生成对抗网络的时间序列生成模型
- Time series generation model based on multi-discriminator generative adversarial network
- 通信学报 2022年43卷第10期页码：167-176
- 作者机构：
  
  1. 郑州大学电气与信息工程学院，河南郑州 450001
  2. 深圳市大数据研究院，广东深圳 518115
- 作者简介：
  
  [ "陆彦辉（1972− ），女，河南许昌人，郑州大学教授，主要研究方向为宽带无线通信理论与系统、无线资源管理和机器学习等" ]
  [ "柳寒（1997− ），男，河南邓州人，郑州大学硕士生，主要研究方向为人工智能与大数据处理、大数据分析与数据挖掘" ]
  [ "李航（1985− ），男，河北承德人，深圳市大数据研究院副研究员，主要研究方向为无线通信与网络、机器学习等" ]
  [ "朱光旭（1989− ），男，广东广州人，深圳市大数据研究院副研究员，主要研究方向为边缘智能、联邦学习、通信感知一体化等" ]
- 基金信息：
  
  国家自然科学基金资助项目(62001310);广东省基础与应用基础研究基金资助项目(2022A1515010109);深圳市科技计划基础研究项目(JCYJ20190813170803617)
- DOI：10.11959/j.issn.1000-436x.2022205
  中图分类号： TP18
- 网络出版日期：2022-10，
  
  纸质出版日期：2022-10-25
- 稿件说明：
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陆彦辉, 柳寒, 李航, 等. 基于多鉴别器生成对抗网络的时间序列生成模型[J]. 通信学报, 2022,43(10):167-176.

Yanhui LU, Han LIU, Hang LI, et al. Time series generation model based on multi-discriminator generative adversarial network[J]. Journal on communications, 2022, 43(10): 167-176.
陆彦辉, 柳寒, 李航, 等. 基于多鉴别器生成对抗网络的时间序列生成模型[J]. 通信学报, 2022,43(10):167-176. DOI： 10.11959/j.issn.1000-436x.2022205.

Yanhui LU, Han LIU, Hang LI, et al. Time series generation model based on multi-discriminator generative adversarial network[J]. Journal on communications, 2022, 43(10): 167-176. DOI： 10.11959/j.issn.1000-436x.2022205.

摘要

摘要：针对时间序列的隐私性和连续性导致时间序列数据集在收集过程中存在收集代价昂贵和数据缺失等问题，提出了一种基于循环神经网络的多鉴别器生成对抗网络模型，该模型能够利用小规模数据集合成得到与真实数据相似分布的时间序列数据集。多鉴别器包含时域、频域、时频域和自相关4种鉴别器，能够充分识别时间序列不同维度下的特征。在实验中，通过损失函数的收敛分析、主成分分析和误差分析，分别从定性和定量的角度对模型进行性能评估。结果表明，所提模型和其他参考模型相比具有更好的性能。

Abstract

Aiming at the problems of expensive collection cost and missing data due to the privacy and continuity of time series data set

a multi-discriminator generative adversarial network model based on recurrent neural network was proposed

which could synthesize time series dataset that were approximately distributed with real data of a small scale dataset.Multi-discriminator included four discriminators in time domain

frequency domain

time-frequency domain and autocorrelation.Different discriminators could effectively recognize the features of the time series in different domains.In the experiment

the convergence of loss function

principal component analysis and error analysis were performed to evaluate the performance of the model from qualitative and quantitative perspectives.The experimental results show that the proposed model has better performance than other reference models.

关键词

Keywords

references

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