AI-Driven Predictive Modeling for Real-Time Economic Forecasting

As a key policy support for development of business, economic forecasting has a principal methodical application for financing streams in financial and government institutions. Research findings highlight that most traditional econometric models have the following types of weaknesses, specifically, in terms of flexibility in handling real time data and accuracy. In this research, the AI approach contains the use of machinelearning methodologies, deep-learning networks, and live feeds of economic data for building a predictive model. We have incorporated two stylistic devices to narrow down the error margin in the prediction, namely high-frequency economics indicators and sentiment analysis. The above findings show that the use of this new technique in prediction is accurate compared to the current econometric models used in the field of econophysics.

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