# Financial Time Series Data Augmentation with Generative Adversarial Networks and Extended Intertemporal Return Plots

arXiv: https://arxiv.org/abs/2205.08924 · HTML: https://mlsanity.com/paper/2205.08924/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 36 references that are in the map.
- First posted on arXiv 18 May 2022.

## Builds on (most cited first, 12 of 12)

- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Wasserstein GAN](https://mlsanity.com/paper/1701.07875/) (2017)
- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs](https://mlsanity.com/paper/1706.02633/) (2017)
- [GRATIS: GeneRAting TIme Series with diverse and controllable characteristics](https://mlsanity.com/paper/1903.02787/) (2019)
- [Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation](https://mlsanity.com/paper/2008.02663/) (2020)
- [Time Series Simulation by Conditional Generative Adversarial Net](https://mlsanity.com/paper/1904.11419/) (2019)
- [Audio Spectrogram Representations for Processing with Convolutional Neural Networks](https://mlsanity.com/paper/1706.09559/) (2017)
- [Leveraging Image-based Generative Adversarial Networks for Time Series Generation](https://mlsanity.com/paper/2112.08060/) (2021)

## Abstract (arXiv)

Data augmentation is a key regularization method to support the forecast and classification performance of highly parameterized models in computer vision. In the time series domain however, regularization in terms of augmentation is not equally common even though these methods have proven to mitigate effects from small sample size or non-stationarity. In this paper we apply state-of-the art image-based generative models for the task of data augmentation and introduce the extended intertemporal return plot (XIRP), a new image representation for time series. Multiple tests are conducted to assess the quality of the augmentation technique regarding its ability to synthesize time series effectively and improve forecast results on a subset of the M4 competition. We further investigate the relationship between data set characteristics and sampling results via Shapley values for feature attribution on the performance metrics and the optimal ratio of augmented data. Over all data sets, our approach proves to be effective in reducing the return forecast error by 7% on 79% of the financial data sets with varying statistical properties and frequencies.