Application of Machine Learning Algorithms for Sustainable Business Management Based on Macro-Economic Data: Supervised Learning Techniques Approach

Muhammad Anees Khan (), Kumail Abbas, Mazliham Mohd Su’ud, Anas A. Salameh, Muhammad Mansoor Alam, Nida Aman, Mehreen Mehreen, Amin Jan (), Nik Alif Amri Bin Nik Hashim and Roslizawati Che Aziz
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Muhammad Anees Khan: Management Studies Department, Bahria Business School, Bahria University, Islamabad 04414, Pakistan
Kumail Abbas: Bahria Business School, Bahria University, Islamabad 04414, Pakistan
Mazliham Mohd Su’ud: Faculty of Computer and Information, Multimedia University, Cyberjaya 50088, Malaysia
Anas A. Salameh: Department of Management Information Systems, College of Business Administration, Prince Sattam Bin Abdul-Aziz University, Al-Kharj 11942, Saudi Arabia
Muhammad Mansoor Alam: Malaysian Institute of Information Technology, Universiti Kuala Lumpur, Kuala Lumpur 50088, Malaysia
Nida Aman: Bahria Business School, Bahria University, Islamabad 04414, Pakistan
Mehreen Mehreen: Department of Management and Humanities, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia
Amin Jan: Faculty of Hospitality, Tourism and Wellness, Universiti Malaysia Kelantan, City Campus, Kota Bharu 16100, Malaysia
Nik Alif Amri Bin Nik Hashim: Faculty of Hospitality, Tourism and Wellness, Universiti Malaysia Kelantan, City Campus, Kota Bharu 16100, Malaysia
Roslizawati Che Aziz: Faculty of Hospitality, Tourism and Wellness, Universiti Malaysia Kelantan, City Campus, Kota Bharu 16100, Malaysia

Sustainability, 2022, vol. 14, issue 16, 1-14

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.

Keywords: macroeconomic factors; machine learning; inflation; supervised machine learning methods; RMSE; MAE; prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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