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

## TL;DR

DLGAN is a novel dual-layer GAN model that effectively synthesizes time series data by capturing temporal dependencies through a two-stage process involving feature extraction and reconstruction, outperforming existing methods.

## Contribution

The paper introduces DLGAN, a dual-layer generative adversarial network that decomposes time series synthesis into feature extraction and reconstruction, improving temporal dependency modeling.

## Key findings

- DLGAN outperforms existing methods on four public datasets.
- The model effectively captures temporal dependencies in generated time series.
- Extensive experiments validate the superiority of DLGAN across various metrics.

## 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.