Forecasting Kazakhstan’s Economic Potential in the Post-Oil Era: A Machine Learning-Based GDP Modeling Approach

This paper analyzes Kazakhstan's economic prospects in the post-oil era using a machine learning-based GDP forecasting technique. The research uses historical GDP data and the Prophet time-series model to forecast Kazakhstan's economic trajectory from 2020 to 2034. The forecast shows moderate fluctuations and long-term uncertainty, but it also points to promising opportunities for economic growth. In order to encourage diversification and long-term growth, the model emphasizes the significance of utilizing Kazakhstan's strategic geopolitical and economic advantages, including its position, wealth of natural resources, and human capital. This study builds a prophet prediction model that foresees a colossal decline in Kazakhstan's GDP from approximately 149 billion KZT in 2020 to about 86 billion KZT by 2034, reflecting a likely long-term economic slump. The downward trend reflects risks associated with continued reliance on oil revenues and poor economic diversification. The 95% confidence interval widens considerably after 2028, echoing increasing uncertainty in projections, particularly amidst global energy transitions. These findings underscore the need for forward-looking economic restructuring and policy reform to attain Kazakhstan's sustainable and resilient development in the post-oil economy. The results show how crucial green technology, creativity, and proactive planning are to building a strong and equitable economy in the future. This data-driven approach aims to support the development of investment plans and policy initiatives that optimize Kazakhstan's potential in a rapidly evolving global economy.

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Publication Details

Journal
Applied computing Journal
Published
2025-05-10
DOI
https://doi.org/10.52098/acj.20255346
Primary Topic
Market Dynamics and Volatility
Type
article
Field-Weighted Citation Impact
0.00
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article

Forecasting Kazakhstan’s Economic Potential in the Post-Oil Era: A Machine Learning-Based GDP Modeling Approach

Eimad Abusham, Kurmangali Aimen Kuanyshbaykyzy, Saad Twaissi, Abu Shawer Omar Ahmad
Applied computing Journal
Market Dynamics and Volatility
article

Forecasting Kazakhstan’s Economic Potential in the Post-Oil Era: A Machine Learning-Based GDP Modeling Approach

Eimad Abusham, Kurmangali Aimen Kuanyshbaykyzy, Saad Twaissi, Abu Shawer Omar Ahmad
article en

Abstract

This paper analyzes Kazakhstan's economic prospects in the post-oil era using a machine learning-based GDP forecasting technique. The research uses historical GDP data and the Prophet time-series model to forecast Kazakhstan's economic trajectory from 2020 to 2034. The forecast shows moderate fluctuations and long-term uncertainty, but it also points to promising opportunities for economic growth. In order to encourage diversification and long-term growth, the model emphasizes the significance of utilizing Kazakhstan's strategic geopolitical and economic advantages, including its position, wealth of natural resources, and human capital. This study builds a prophet prediction model that foresees a colossal decline in Kazakhstan's GDP from approximately 149 billion KZT in 2020 to about 86 billion KZT by 2034, reflecting a likely long-term economic slump. The downward trend reflects risks associated with continued reliance on oil revenues and poor economic diversification. The 95% confidence interval widens considerably after 2028, echoing increasing uncertainty in projections, particularly amidst global energy transitions. These findings underscore the need for forward-looking economic restructuring and policy reform to attain Kazakhstan's sustainable and resilient development in the post-oil economy. The results show how crucial green technology, creativity, and proactive planning are to building a strong and equitable economy in the future. This data-driven approach aims to support the development of investment plans and policy initiatives that optimize Kazakhstan's potential in a rapidly evolving global economy.

Applied computing Journal
Openalex Percentile: Top 85%
Market Dynamics and Volatility
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