eprintid: 16503
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/65/03
datestamp: 2023-12-19 03:23:01
lastmod: 2023-12-19 03:23:01
status_changed: 2023-12-19 03:06:22
type: article
metadata_visibility: show
creators_name: Khan, M.A.
creators_name: Abbas, K.
creators_name: Su�ud, M.M.
creators_name: Salameh, A.A.
creators_name: Alam, M.M.
creators_name: Aman, N.
creators_name: Mehreen, M.
creators_name: Jan, A.
creators_name: Hashim, N.A.A.B.N.
creators_name: Aziz, R.C.
title: Application of Machine Learning Algorithms for Sustainable Business Management Based on Macro-Economic Data: Supervised Learning Techniques Approach
ispublished: pub
keywords: algorithm; artificial neural network; business; economic development; exchange rate; experimental study; inflation; management practice; nearest neighbor analysis; regression analysis; support vector machine; sustainability
note: cited By 6
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. © 2022 by the authors.
date: 2022
publisher: MDPI
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137662628&doi=10.3390%2fsu14169964&partnerID=40&md5=8608e895c607a8142c576e10d59758d9
id_number: 10.3390/su14169964
full_text_status: none
publication: Sustainability (Switzerland)
volume: 14
number: 16
refereed: TRUE
issn: 20711050
citation:   Khan, M.A. and Abbas, K. and Su�ud, M.M. and Salameh, A.A. and Alam, M.M. and Aman, N. and Mehreen, M. and Jan, A. and Hashim, N.A.A.B.N. and Aziz, R.C.  (2022) Application of Machine Learning Algorithms for Sustainable Business Management Based on Macro-Economic Data: Supervised Learning Techniques Approach.  Sustainability (Switzerland), 14 (16).   ISSN 20711050