A machine learning approach to forecast international trade: The case of Croatia

Background: This paper presents a machine learning approach to forecast Croatia's international bilateral trade. Objectives: The goal of this paper is to evaluate the performance of machine learning algorithms in predicting international bilateral trade flows related to imports and exports in t...

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Bibliographic Details
Published in:Business Systems Research Vol. 13; no. 3; pp. 144 - 160
Main Authors: Jošić, Hrvoje, Žmuk, Berislav
Format: Journal Article Paper
Language:English
Published: Warsaw Sciendo 01.10.2022
University of Zagreb, Faculty of Business and Economics
IRENET, Udruga za promicanje istraživanja i inovacija u ekonomiji
Subjects:
Algorithms
B17
C45
Croatia
Decision making
Exports
Forecasting
Gaussian process
Imports
International trade
Machine learning
MAPE
Multilayer perceptron
Multilayer perceptrons
Neural networks
Performance evaluation
WEKA
Croatia
ISSN:1847-9375, 1847-8344, 1847-9375
Online Access:Get full text
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Summary:Background: This paper presents a machine learning approach to forecast Croatia's international bilateral trade. Objectives: The goal of this paper is to evaluate the performance of machine learning algorithms in predicting international bilateral trade flows related to imports and exports in the case of Croatia. Methods/Approach: The dataset on Croatian bilateral trade with over 180 countries worldwide from 2001 to 2019 is assembled using main variables from the gravity trade model. To forecast values of Croatian bilateral exports and imports for a horizon of one year (the year 2020), machine learning algorithms (Gaussian processes, Linear regression, and Multilayer perceptron) have been used. Each forecasting algorithm is evaluated by calculating mean absolute percentage errors (MAPE). Results: It was found that machine learning algorithms have a very good predicting ability in forecasting Croatian bilateral trade, with neural network Multilayer perceptron having the best performance among the other machine learning algorithms. Conclusions Main findings from this paper can be important for economic policymakers and other subjects in this field of research. Timely information about the changes in trends and projections of future trade flows can significantly affect decision-making related to international bilateral trade flows.
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ISSN:1847-9375
1847-8344
1847-9375
DOI:10.2478/bsrj-2022-0030