Prediction of Economic Growth by Using Machine-Learning Algorithms through Sentiment Index Analysis in Economy of Pakistan

Abstract

The neural network method's ability to mimic nonlinear communications without prior information is a major benefit for accurate function approximation. It differs from ARIMA and linear regression, which need linearity and may not work for all data sorts. Compared to (seasonal) ARIMA models, neural networks yield similar results. Neural networks also model and predict multivariate time series using causal and co-integration research. In contrast to the traditional economic forecasting approach, machine-learning techniques have prioritized precise forecasting without predetermined assumptions or conclusions. Owing to their enhanced predictive abilities and technological advancements, they provide more adaptability and have gained widespread acceptance in several fields. In terms of estimating US real estate values, machine-learning technology fared better than traditional econometric models, as shown. Total there were 131601 economic and financial news, which may include the phrases along with the names specified by the bank, and it may also include local media and mainstream. Machine learning uses the strength of data-driven algorithms and state-of-the-art computational techniques to improve decision-making in a range of industries. The findings of this research contribute to better forecasting practices and show how machine learning may provide more accurate forecasts in real-world scenarios.