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

• Published in: Sustainability 2022-08, Vol.14 (16), p.9964
• Main Authors: Khan, Muhammad Anees, Abbas, Kumail, Su’ud, Mazliham Mohd, Salameh, Anas A, Alam, Muhammad Mansoor, Aman, Nida, Mehreen, Mehreen, Jan, Amin, Hashim, Nik Alif Amri Bin Nik, Aziz, Roslizawati Che
• Format: Article
• Language: English
• Subjects: Algorithms; Archives & records; Artificial intelligence; Automation; Business administration; Business machines; Currency; Data mining; Economic forecasting; Forecasting; Forecasting techniques; Forecasts and trends; Foreign exchange; Foreign exchange markets; Foreign exchange rates; GDP; Gross Domestic Product; Inflation; Inflation (Finance); Learning algorithms; Machine learning; Macroeconomics; Malaysia; Management consultants; Methods; Multinational corporations; Neural networks; Pakistan; Polynomials; Prices and rates; Root-mean-square errors; Statistical methods; Supervised learning; Support vector machines; Sustainability; Technology application; Test sets; Unemployment
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su14169964

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.