基于多鉴别器生成对抗网络的时间序列生成模型

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

Abstract

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.

Key words: generative adversarial network, time series, Fourier transform, autocorrelation function, machine learning

CLC Number:

TP18

Yanhui LU, Han LIU, Hang LI, Guangxu ZHU. Time series generation model based on multi-discriminator generative adversarial network[J]. Journal on Communications, 2022, 43(10): 167-176.

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训练模型	MSE	RMSE	MAE	MAPE
MDGAN	1.175 438	1.082 374	0.863 169	7.125 314
频域鉴别器GAN	1.330 979	1.180 982	1.147 531	8.293 083
时频域鉴别器GAN	1.293 977	1.271 668	1.136 859	8.570 859
自相关鉴别器GAN	1.411 853	1.310 379	1.362 662	9.135 859

训练模型	MSE	RMSE	MAE	MAPE
MDGAN	1.175 438	1.082 374	0.844 753	7.047 909
TimeGAN	1.182 172	1.106 035	0.863 169	7.125 314
SigCWGAN	1.241 732	1.111 821	0.907 567	7.405 342
RCGAN	1.388 121	1.234 397	1.021 464	8.490 861

训练模型	MSE	RMSE	MAE	MAPE
MDGAN	0.002 614	0.035 102	0.028 508	11.761 79
TimeGAN	0.002 981	0.051 701	0.041 101	20.810 77
SigCWGAN	0.003 176	0.050 541	0.041 165	20.214 82
RCGAN	0.004 475	0.066 354	0.054 091	28.773 71

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

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