电力信息与通信技术2026，Vol.24Issue(8)：27-35,9.DOI:10.16543/j.2095-641x.electric.power.ict.2026.08.03
基于MAD-TimeGAN数据增强的小样本时间序列预测方法
Small Sample Time Series Prediction Method Based on MAD-TimeGAN Data Enhancement
摘要
Abstract
In response to the insufficient data problem of small sample time series prediction,during the training process of the existing TimeGAN model,once the generator learns to generate some samples that can effectively deceive the discriminator,it tends to generate these samples repeatedly instead of exploring other areas of the data distribution,resulting in insufficient diversity of generated samples,which in turn causes pattern crash problems.To this end,this paper proposes a multi-agent time series generalized adversarial networks(MAD-TimeGAN).The network uses a multi-agent diverse generative adversarial network(MAD-GAN)to replace the original GAN network.MAD-GAN includes multiple generators and a discriminator.Multiple generators are used to generate data in different modes.The discriminator is used to determine the authenticity of the data and determine which generator the generated samples come from.By introducing this network,it can not only effectively alleviate the pattern crash problem of TimeGAN,but also increase the diversity of generated samples and obtain higher-quality generated samples.To verify the effectiveness of the proposed method,data enhancement experiments were performed on multiple experimental data sets and the prediction effect of the generated data was observed when the prediction was predicted.Experimental results show that compared with the unimproved model,the proposed data enhancement method can better cover the original data and improve prediction accuracy.关键词
多智能体/时间序列预测/小样本/数据增强/MAD-TimeGANKey words
multi-agent/time series prediction/small sample size/data enhancement/MAD-TimeGAN分类
信息技术与安全科学引用本文复制引用
尼俊红,孙浩,荀超,陈延滔,石超君..基于MAD-TimeGAN数据增强的小样本时间序列预测方法[J].电力信息与通信技术,2026,24(8):27-35,9.基金项目
国家自然科学基金项目(62206095) （62206095）
国网福建省电力有限公司经济技术研究院科技项目(SGFJJY00NYJS2400093). （SGFJJY00NYJS2400093）
