DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks

• URL: http://arxiv.org/abs/2508.21340v1
• Date: Fri, 29 Aug 2025 05:58:36 GMT
• Title: DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks
• Authors: Xuan Hou, Shuhan Liu, Zhaohui Peng, Yaohui Chu, Yue Zhang, Yining Wang,
• Abstract summary: We propose a simple but effective generative model textbfDual-textbfLayer textbfGenerative textbfAdversarial textbfNetworks, named textbfDLGAN.<n>The model decomposes the time series generation process into two stages: sequence feature extraction and sequence reconstruction. First, these two stages form a complete time series autoencoder, enabling supervised learning on the original time series to ensure that the reconstruction process can restore the temporal dependencies of the sequence.
• Score: 13.345872524896722
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Time series synthesis is an effective approach to ensuring the secure circulation of time series data. Existing time series synthesis methods typically perform temporal modeling based on random sequences to generate target sequences, which often struggle to ensure the temporal dependencies in the generated time series. Additionally, directly modeling temporal features on random sequences makes it challenging to accurately capture the feature information of the original time series. To address the above issues, we propose a simple but effective generative model \textbf{D}ual-\textbf{L}ayer \textbf{G}enerative \textbf{A}dversarial \textbf{N}etworks, named \textbf{DLGAN}. The model decomposes the time series generation process into two stages: sequence feature extraction and sequence reconstruction. First, these two stages form a complete time series autoencoder, enabling supervised learning on the original time series to ensure that the reconstruction process can restore the temporal dependencies of the sequence. Second, a Generative Adversarial Network (GAN) is used to generate synthetic feature vectors that align with the real-time sequence feature vectors, ensuring that the generator can capture the temporal features from real time series. Extensive experiments on four public datasets demonstrate the superiority of this model across various evaluation metrics.

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