The Role of Time Series Forecasting in Enhancing the Predictive Power of Generative Artificial Intelligence Models: A Comprehensive Review

Abstract

Time series forecasting plays a crucial role in advancing the predictive power of generative artificial intelligence (Gen AI) models, significantly impacting their decision-making, creativity, and overall performance. By leveraging the understanding of temporal patterns and dependencies, Gen AI systems can enhance their capabilities in diverse domains such as natural language processing (NLP) and image generation. This comprehensive review aims to explore the profound impact of time series forecasting on improving the quality and consistency of Gen AI outputs. Understanding how time series prediction contributes to the performance of Gen AI models in sectors like NLP and image generation is essential in unlocking their full potential. However, the integration of time series forecasting with Gen AI poses challenges such as computational complexity and biases affecting the model outputs. Addressing these challenges is crucial to ensure accurate and reliable outcomes. Future research directions should focus on optimizing computational needs, mitigating biases, and enhancing the ethical implications of Gen AI systems utilizing time series forecasting to further advance their capabilities and ensure trustworthy applications in various fields.