Analyzing the Evolution of AIGenerated Art Styles Using Time Series Analysis: A Trend Study on NFT Artworks - Maidin, Siti Sarah; Yang, Qingxue; Samson, A Sunil (Bright Publisher, 2025)
This study investigates the development of AI-generated art styles within the growing non-fungible token (NFT) market. Using time series analysis, the research identifies key trends and shifts in art styles from 2022 to 2024, revealing how various art forms, algorithms, and mediums evolved in response to technological advancements and market forces. Data was collected from a sample of 10,000 NFT artworks, categorized by creation date, style, and algorithm usage. Exploratory Data Analysis (EDA) techniques, including line graphs and heatmaps, were employed to visualize and interpret trends across different art styles and AI tools. Results indicate a significant increase in the popularity of styles like surrealism and realism, with deepdream and GANpaint algorithms being frequently associated with these styles. Stacked area charts further highlighted the proportional growth of art styles over time, providing insights into both short-term popularity spikes and long-term trends. The findings suggest that the integration of AI algorithms significantly influenced the rise of specific art genres, with certain algorithms correlating strongly with particular styles. Practical implications for artists and collectors include the potential for data-driven insights to guide creative choices and investment strategies. The study's limitations, such as the lack of broader market data, provide a foundation for future research to explore the intersection of AI-generated art, NFT marketplaces, and cultural influences. The paper concludes that AI and NFTs are reshaping the traditional art market, presenting new opportunities for creativity, ownership, and artistic value in a digital age.