A comparative analysis and prediction of the economic growth of Pakistan using machine learning models

Abstract

This article investigates a comparative analysis of machine learning models for Pakistan's Gross Domestic Product (GDP), an important indicator of the nation's economic development. GDP is crucial to assess well-versed decisions. Since machine learning techniques are more sophisticated, much interest has been developed in predicting GDP to handle complex data patterns and enhance prediction accuracy. In this study, we evaluated the performance of a variety of machine learning algorithms like Auto-Regressive Integrated Moving Average (ARIMA), double exponential smoothing, Multilayer Perceptron (MLP), Neural Network Auto-Regressive (NNAR), and hybrid machine learning models on data from 1960 to 2022. The MLP used in Artificial Neural Networks (ANNs) outperforms based on the outcomes. This comparative analysis provides insights into the most suitable model for accurate prediction of Pakistani GDP for the years 2023 to 2032. This article provides a detailed analysis of various machine learning models used to predict Pakistan's GDP accurately. GDP prediction is an essential indicator of a nation's economic development and is crucial in making informed decisions. With the advancements in machine learning techniques, there has been a growing interest in predicting GDP due to their efficiency in handling complex data patterns and improving prediction accuracy.