鉴别器
插补(统计学)
缺少数据
计算机科学
发电机(电路理论)
生成对抗网络
人工神经网络
插值(计算机图形学)
生成语法
时间序列
人工智能
算法
数据挖掘
机器学习
模式识别(心理学)
深度学习
运动(物理)
电信
功率(物理)
物理
量子力学
探测器
作者
Yunsheng Wang,Xinghan Xu,Lei Hu,Jianchao Fan,Min Han
标识
DOI:10.1016/j.knosys.2023.111215
摘要
Generative adversarial networks (GANs) have been widely utilized in time series analysis and modeling, wherein generators and discriminators interact to generate realistic data. However, when addressing the challenge of imputing continuous missing values in time series, the generator struggles to learn meaningful features due to the loss of local information, and the discriminator's loss function exhibits significant deviations in determining the probability, making it difficult to effectively update model parameters during training. In response to these issues, this study presents a novel imputation model called cue Wasserstein generative adversarial network with gradient penalty (CWGAIN-GP). CWGAIN-GP incorporates the contextual cue information matrixs into the generator, enabling obtain and capture the potential data evolution features that hidden beyond the missing positions. This approach constrains the generator's generation results to be closer to the true values probability distribution. Meanwhile, it optimizes the generator and discriminator neural network structure and loss function computation strategy of the original GAN model, which not only improves the accuracy of continuous missing data interpolation, but also improves the training stability of the model. Finally, We used three imputation accuracies on two real-world datasets, including experiments on imputation accuracy for eight comparison models, ablation study, and experiments on the effect of consecutive missing lengths. The experimental results show that CWGAIN-GP achieves impressive performance in imputing continuous missing time series.
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