Generative AI in Predictive Analytics: Transforming Business Intelligence Through Enhanced Forecasting Techniques

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@article{1705025,
      author = {Swetha Chinta},
      title = {Generative AI in Predictive Analytics: Transforming Business Intelligence Through Enhanced Forecasting Techniques},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {7},
      number = {3},
      pages = {665-677},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1705025.pdf},
      abstract = {The emergence of Generative AI has revolutionized the landscape of predictive analytics, offering new methodologies and enhanced capabilities for business intelligence. This paper explores the integration of generative models into predictive analytics frameworks, emphasizing their potential to improve forecasting accuracy and decision-making processes in various industries. By leveraging advanced algorithms, including Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), organizations can generate synthetic data that enriches existing datasets, thereby addressing issues related to data scarcity and enhancing model training. The study highlights case studies demonstrating the effectiveness of generative AI in areas such as demand forecasting, risk assessment, and customer behavior analysis. Furthermore, we discuss the implications of adopting generative AI technologies for strategic business decisions, emphasizing the need for robust data governance and ethical considerations in their deployment.},
      keywords = {Generative AI, Predictive Analytics, Business Intelligence, Forecasting Techniques, Data Enrichment},
      month = {September},
  }