Citation

Khan, Muhammad Anees and Abbas, Kumail and Mohd Su'ud, Mazliham and Salameh, Anas A. and Alam, Muhammad Mansoor and Aman, Nida and Mehreen, Mehreen and Jan, Amin and Nik Hashim, Nik Alif Amri and Che Aziz, Roslizawati (2022) Application of Machine Learning Algorithms for Sustainable Business Management Based on Macro-Economic Data: Supervised Learning Techniques Approach. Sustainability, 14 (16). p. 9964. ISSN 2071-1050

Text sustainability-14-09964.pdf - Published Version Restricted to Repository staff only Download (2MB)

Abstract

Macroeconomic indicators are the key to success in the development of any country and are very much important for the overall economy of any country in the world. In the past, researchers used the traditional methods of regression for estimating macroeconomic variables. However, the advent of efficient machine learning (ML) methods has led to the improvement of intelligent mechanisms for solving time series forecasting problems of various economies around the globe. This study focuses on forecasting the data of the inflation rate and the exchange rate of Pakistan from January 1989 to December 2020. In this study, we used different ML algorithms like k-nearest neighbor (KNN), polynomial regression, artificial neural networks (ANNs), and support vector machine (SVM). The data set was split into two sets: the training set consisted of data from January 1989 to December 2018 for the training of machine algorithms, and the remaining data from January 2019 to December 2020 were used as a test set for ML testing. To find the accuracy of the algorithms used in the study, we used root mean square error (RMSE) and mean absolute error (MAE). The experimental results showed that ANNs archives the least RMSE and MAE compared to all the other algorithms used in the study. While using the ML method for analyzing and forecasting inflation rates based on error prediction, the test set showed that the polynomial regression (degree 1) and ANN methods outperformed SVM and KNN. However, on the other hand, forecasting the exchange rate, SVM RBF outperformed KNN, polynomial regression, and ANNs.

Item Type:	Article
Uncontrolled Keywords:	Macroeconomic factors, machine learning, inflation
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	06 Oct 2022 03:41
Last Modified:	06 Oct 2022 03:55
URII:	http://shdl.mmu.edu.my/id/eprint/10468

Downloads

Edit (login required)