AI-ENABLED MARKET FORECASTING FOR FOLK ART INDUSTRIES
DOI:
https://doi.org/10.29121/shodhkosh.v6.i3s.2025.6757Keywords:
AI Forecasting, Folk Art Economy, LSTM–Prophet–GBR Model, Cultural Data Analytics, Sentiment Analysis, Policy Integration, Ethical AI, Predictive GovernanceAbstract [English]
The study provides a holistic framework of AI-enabled market forecasting within the folk-art industry based on a combination of machine learning, statistical modeling, and cultural data analytics to forecast the market forces and facilitate sustainable artisan livelihoods. The study presents a unified ensemble framework that integrates Long Short-Term Memory (LSTM) networks, Prophet time-series models, and Gradient Boosting Regression (GBR) to solve the non-linearity, seasonality, and sentiment nature of the folk-art markets. The information was obtained through e-commerce sites, cultural fairs, cooperative registries and sentiment streams in social network, to be processed in a single common data ecology infrastructured to achieve cultural interpretability and computational efficiency. The results of the experiment showed that the ensemble invested significantly more (R 2 = 0.94, reducing RMSE by 15) than the individual models, and integrating the temporal trends, emotional feedback, and regional heterogeneity in the ensemble was justified. Another concept presented by the study was the Policy Integration Framework of AI in Folk Art Governance, which connects predictive analytics and decisions of artisans, traders, and policymakers. Biases were integrated with ethical concerns via the detection of ethical aspects concerning the use of AI and consent-based data management, as well as the enhancing of transparency, thus making AI use in heritage ecosystems responsible. The results also indicate the potential of AI to revolutionize artisans by enabling them to foresee their market, maximize their trading connections, and inform the evidence-based production of cultural policy. There is a long-term vision of this study that a Cultural Intelligence Network (CIN) of interconnected infrastructure can be formed in which AI, human imagination, and cultural governance are integrated to maintain authenticity and facilitate adaptive, data-driven development in the world folk art economy.
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Copyright (c) 2025 Yogesh; Avishi Mohta, Mukesh Parashar, Sukhman Ghumman, Divya Sharma, Dr. Shruthi K Bekal, Vijaykumar Bhanuse
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