mlsanityData to 14 Oct 2026

Simulating financial time series using attention

arXiv 2207.00493 ↗First posted 1 Jul 2022q-fin.ST cs.LG q-fin.CPcited by 4

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Abstract (arXiv)

Financial time series simulation is a central topic since it extends the limited real data for training and evaluation of trading strategies. It is also challenging because of the complex statistical properties of the real financial data. We introduce two generative adversarial networks (GANs), which utilize the convolutional networks with attention and the transformers, for financial time series simulation. The GANs learn the statistical properties in a data-driven manner and the attention mechanism helps to replicate the long-range dependencies. The proposed GANs are tested on the S&P 500 index and option data, examined by scores based on the stylized facts and are compared with the pure convolutional GAN, i.e. QuantGAN. The attention-based GANs not only reproduce the stylized facts, but also smooth the autocorrelation of returns.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 26 of its 44 references that are in the map. First posted on arXiv 1 Jul 2022.

Builds on

26 of 44 references.

References · 26

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14 more
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Cited by year

Citing papers per year: 2024: 2; 2025: 2.

4 papers.

  1. 2024Time-Causal VAE: Robust Financial Time Series Generatorcited by 8
  2. 2024Universal randomised signatures for generative time series modellingcited by 4
  3. 2025Synthetic Data for Portfolios: A Throw of the Dice Will Never Abolish Chancecited by 4
  4. 2025Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluationcited by 2