{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T16:23:32Z","timestamp":1778084612133,"version":"3.51.4"},"reference-count":36,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:00:00Z","timestamp":1760486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research on Online Deep Transfer Learning of Time Series based on Financial Data","award":["Qian Ke He Ji Chu [2024] general 52"],"award-info":[{"award-number":["Qian Ke He Ji Chu [2024] general 52"]}]},{"name":"Guizhou Province Basic Research Plan (Natural Sciences) Projects"},{"name":"Sixth Batch of Gui-Zhou Province High-level Innovative Talent Training Program","award":["Zhu Ke He Tong [2022]011"],"award-info":[{"award-number":["Zhu Ke He Tong [2022]011"]}]},{"name":"Guiyang University New Degree Awarding Point Cultivation and Construction Project in 2025","award":["Gyxk202502"],"award-info":[{"award-number":["Gyxk202502"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>With the rapid growth of FinTech, time-series data has become pervasive in financial markets. However, the nonstationarity, high noise levels, and complex temporal dependencies of financial data pose significant challenges to the efficacy and stability of standard generative models. To overcome these limitations, we propose MiT-WGAN, a gradient-penalized Wasserstein generative adversarial network that integrates a multi-convolutional dynamic fusion (MCDF) module in parallel with an enhanced Transformer (iTransformer) to jointly capture local patterns and long-range dependencies. We evaluate MiT-WGAN on S&amp;P 500 stock trading data using a comprehensive set of baseline models and evaluation metrics. Experimental results demonstrate that MiT-WGAN achieves superior sample quality, better preservation of statistical properties, and improved training stability, confirming its effectiveness for financial time-series modeling.<\/jats:p>","DOI":"10.3390\/sym17101740","type":"journal-article","created":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T10:12:29Z","timestamp":1760523149000},"page":"1740","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["MiT-WGAN: Financial Time-Series Generation GAN Based on Multi-Convolution Dynamic Fusion and iTransformer"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-8128-6372","authenticated-orcid":false,"given":"Lin","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Electronic Information Engineering, Guiyang University, Guiyang 550005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4622-0724","authenticated-orcid":false,"given":"Chunji","family":"Long","sequence":"additional","affiliation":[{"name":"School of Electronic Information Engineering, Guiyang University, Guiyang 550005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"20200209","DOI":"10.1098\/rsta.2020.0209","article-title":"Time-series forecasting with deep learning: A survey","volume":"379","author":"Lim","year":"2021","journal-title":"Philos. 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