Enhancing Time Series Forecasting in Low-Liquidity Markets Using Generative Adversarial Networks

Title

Enhancing Time Series Forecasting in Low-Liquidity Markets Using Generative Adversarial Networks

Creator

Dutta, Neelanjan; Prasad, Smrity; Kalaivani, S.

Description

Financial assets that are low liquidity are very difficult to forecast as they are sparsely traded, their volatility is not regular, and scarce historic evidence exists. This paper will explore the hypothesis of whether in this kind of limited environment, generative models can enhance the effectiveness of forecasting. A dual model framework is constructed which contrasts a normal Long Short Term Memory (LSTM) network with TimeGAN based synthetic data augmentation method in 60-day long-range forecasting of the TRY/USD exchange rate. The methodology consists in the training of an LSTM model on real historical sequences and the improvement with TimeGAN generated synthetic sequences with a maintained temporal structure. It has been shown that TimeGAN has a significant effect on the accuracy of the forecasts, the RMSE decreased to 0.0002 by approximately fifty percent, and the R2 grew to 0.9921 by approximately fifty percent. The results suggest that augmentation through GAN enhances generalization of models in thin and dynamic markets. The most important contributions include implementation of TimeGAN to low-liquidity FX forecasting, the assessment of the effects of synthetic data on forecast accuracy and the empirical benchmark of LSTM and TimeGAN in low-volume finance. 2025 IEEE.

Source

4th International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2025;pp.933-938

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Exchange rate prediction; Financial markets; GANs; Low liquidity assets; LSTM; Synthetic data; Time series forecasting; TimeGAN

Coverage

Dutta N., Christ (Deemed to be University), Dept. of Statistics and Data Science, Bengaluru, India; Prasad S., Christ (Deemed to be University), Dept. of Statistics and Data Science, Bengaluru, India; Kalaivani S., Christ (Deemed to be University), Dept. of Statistics and Data Science, Bengaluru, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833156587-9;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICAAIC64647.2025.11331192

https://www.scopus.com/pages/publications/105034868013?origin=resultslist