Time series generation model based on multi-discriminator generative adversarial network

Yanhui LU; Han LIU; Hang LI; Guangxu ZHU

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

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Time series generation model based on multi-discriminator generative adversarial network

Papers | 更新时间：2024-06-05

- Time series generation model based on multi-discriminator generative adversarial network
- Journal on Communications Vol. 43, Issue 10, Pages: 167-176(2022)
- 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：
- 基金信息：
  
  The National Natural Science Foundation of China(62001310);The Foundation for Basic and Applied Basic Research of Guangdong Province(2022A1515010109);The Basic Research Project of Shenzhen Science and Technology Plan(JCYJ20190813170803617)
- DOI：10.11959/j.issn.1000-436x.2022205
  CLC： TP18
- Online First：2022-10，
  
  Published：25 October 2022
- 稿件说明：
移动端阅览
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：

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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