Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Dutta, Neelanjan; Prasad, Smrity; Kalaivani, S. Title A name given to the resource Enhancing Time Series Forecasting in Low-Liquidity Markets Using Generative Adversarial Networks Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 4th International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2025;pp.933-938 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICAAIC64647.2025.11331192" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICAAIC64647.2025.11331192</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034868013?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034868013?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource Exchange rate prediction; Financial markets; GANs; Low liquidity assets; LSTM; Synthetic data; Time series forecasting; TimeGAN Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833156587-9; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online