# SeriesGAN: Time Series Generation via Adversarial and Autoregressive Learning

arXiv: https://arxiv.org/abs/2410.21203 · HTML: https://mlsanity.com/paper/2410.21203/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 29 references that are in the map.
- First posted on arXiv 28 Oct 2024.

## Builds on (most cited first, 16 of 16)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks](https://mlsanity.com/paper/1506.03099/) (2015)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs](https://mlsanity.com/paper/1706.02633/) (2017)
- [A note on the evaluation of generative models](https://mlsanity.com/paper/1511.01844/) (2015)
- [Professor Forcing: A New Algorithm for Training Recurrent Networks](https://mlsanity.com/paper/1610.09038/) (2016)
- [C-RNN-GAN: Continuous recurrent neural networks with adversarial training](https://mlsanity.com/paper/1611.09904/) (2016)
- [catch22: CAnonical Time-series CHaracteristics](https://mlsanity.com/paper/1901.10200/) (2019)
- [On the Quantitative Analysis of Decoder-Based Generative Models](https://mlsanity.com/paper/1611.04273/) (2016)
- [T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular Sampling](https://mlsanity.com/paper/1811.08295/) (2018)
- [Relational Autoencoder for Feature Extraction](https://mlsanity.com/paper/1802.03145/) (2018)
- [Contrastive Representation Learning for Predicting Solar Flares from Extremely Imbalanced Multivariate Time Series Data](https://mlsanity.com/paper/2410.00312/) (2024)
- [Enhancing Multivariate Time Series-based Solar Flare Prediction with Multifaceted Preprocessing and Contrastive Learning](https://mlsanity.com/paper/2409.14016/) (2024)

## Built on by (most cited first, 6 of 6)

- [Rejoining fragmented ancient bamboo slips with physics-driven deep learning](https://mlsanity.com/paper/2505.08601/) (2025)
- [Towards Time Series Generation Conditioned on Unstructured Natural Language](https://mlsanity.com/paper/2506.22927/) (2025)
- [TIMED: Adversarial and Autoregressive Refinement of Diffusion-Based Time Series Generation](https://mlsanity.com/paper/2509.19638/) (2025)
- [Privacy-Preserving Generative Modeling and Clinical Validation of Longitudinal Health Records for Chronic Disease](https://mlsanity.com/paper/2512.00434/) (2025)
- [A GAN-Based Framework for Robust Data Synthesis in Satellite Internet Observations](https://mlsanity.com/paper/2607.24790/) (2026)
- [StatD2GAN: When Calibration Masks Generator Quality in Held-Out Evaluation of Synthetic Weather Sequences](https://mlsanity.com/paper/2609.33761/) (2026)

## Abstract (arXiv)

Current Generative Adversarial Network (GAN)-based approaches for time series generation face challenges such as suboptimal convergence, information loss in embedding spaces, and instability. To overcome these challenges, we introduce an advanced framework that integrates the advantages of an autoencoder-generated embedding space with the adversarial training dynamics of GANs. This method employs two discriminators: one to specifically guide the generator and another to refine both the autoencoder's and generator's output. Additionally, our framework incorporates a novel autoencoder-based loss function and supervision from a teacher-forcing supervisor network, which captures the stepwise conditional distributions of the data. The generator operates within the latent space, while the two discriminators work on latent and feature spaces separately, providing crucial feedback to both the generator and the autoencoder. By leveraging this dual-discriminator approach, we minimize information loss in the embedding space. Through joint training, our framework excels at generating high-fidelity time series data, consistently outperforming existing state-of-the-art benchmarks both qualitatively and quantitatively across a range of real and synthetic multivariate time series datasets.