[ La régression linéaire et la prédiction de l’inflation monétaire: Application à l’intention de la Banque Centrale du Congo (RDC) ]
Volume 40, Issue 3, September 2023, Pages 573–581
KABEYA TSHISEBA Cedric1, Christ TSUNGU MIJIMBU2, Glad LUFIMPU3, and Christian MUABI KATENDA4
1 Département de Mathématique et Informatique, Faculté de Sciences, Université Pédagogique Nationale (UPN), Ngaliema, Kinshasa, RD Congo
2 Université Pédagogique Nationale (UPN), RD Congo
3 Université Pédagogique Nationale (UPN), RD Congo
4 Université Pédagogique Nationale (UPN), RD Congo
Original language: French
Copyright © 2023 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
With the advent of automatic learning methods and the exponential growth of computer power, several things are being facilitated, in particular the prediction of certain behaviors, made by banking establishments. Although some prediction tools such as Excel are nowadays used by some of our establishments, we find that machine learning remains unused so far, even less with all its power, yet many advantages and opportunities present themselves to it. use. We argue that elsewhere, experiments based on machine learning, in other words automatic learning, are more than topical, even more so in the banking sector. We are therefore going through this experience, to propose as an illustration and educational, for our local banking establishments, an activity of prediction of monetary inflation, using the power of artificial intelligence. The prediction in question here will be made on the basis of a set of data collected from a few banks in the city-province of Kinshasa, and particular emphasis will be placed on general consumer price indices. It is important to note that the prediction in question here goes as far as clearly specifying the causes of the inflation being analyzed, or predicted, of course on the basis of the different variations of the indicators. The machine learning used here offers us several possibilities in terms of algorithms and models, but in the context of this work, we will only address a few, in particular Linear Regression, the Random Forest Regression algorithm or Radom Forest Regression, and the Regression model decision tree, will get we will get the best algorithm with respect to its score.
Author Keywords: Machine Learning, Monetary Inflation and Artificial Intelligence.
Volume 40, Issue 3, September 2023, Pages 573–581
KABEYA TSHISEBA Cedric1, Christ TSUNGU MIJIMBU2, Glad LUFIMPU3, and Christian MUABI KATENDA4
1 Département de Mathématique et Informatique, Faculté de Sciences, Université Pédagogique Nationale (UPN), Ngaliema, Kinshasa, RD Congo
2 Université Pédagogique Nationale (UPN), RD Congo
3 Université Pédagogique Nationale (UPN), RD Congo
4 Université Pédagogique Nationale (UPN), RD Congo
Original language: French
Copyright © 2023 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
With the advent of automatic learning methods and the exponential growth of computer power, several things are being facilitated, in particular the prediction of certain behaviors, made by banking establishments. Although some prediction tools such as Excel are nowadays used by some of our establishments, we find that machine learning remains unused so far, even less with all its power, yet many advantages and opportunities present themselves to it. use. We argue that elsewhere, experiments based on machine learning, in other words automatic learning, are more than topical, even more so in the banking sector. We are therefore going through this experience, to propose as an illustration and educational, for our local banking establishments, an activity of prediction of monetary inflation, using the power of artificial intelligence. The prediction in question here will be made on the basis of a set of data collected from a few banks in the city-province of Kinshasa, and particular emphasis will be placed on general consumer price indices. It is important to note that the prediction in question here goes as far as clearly specifying the causes of the inflation being analyzed, or predicted, of course on the basis of the different variations of the indicators. The machine learning used here offers us several possibilities in terms of algorithms and models, but in the context of this work, we will only address a few, in particular Linear Regression, the Random Forest Regression algorithm or Radom Forest Regression, and the Regression model decision tree, will get we will get the best algorithm with respect to its score.
Author Keywords: Machine Learning, Monetary Inflation and Artificial Intelligence.
Abstract: (french)
Avec l’avènement des méthodes d’apprentissage automatique et la croissance exponentielle de la puissance des ordinateurs, plusieurs choses se sont vues être facilitées, notamment la prédiction de certains comportements, faite par les établissements bancaires. Bien que certains outils de prédictions comme Excel sont à ces jours utilisés par certains de nos établissements, nous constatons que l’apprentissage automatique reste jusqu’ici pas exploitée, moins encore avec toute sa puissance, pourtant des nombreux avantages et opportunités se présentent à son utilisation. Nous avouons que sous d’autres cieux, les expériences basées sur le machine learning, autrement dit apprentissage automatique, sont plus que d’actualité, plus encore dans le secteur bancaire. Nous allons donc à travers cette expérience, proposer à titre d’illustration et pédagogique, pour nos établissements bancaires de la place, une activité de prédiction de l’inflation monétaire, usant de la puissance de l’intelligence artificielle. La prédiction dont il est question ici sera faite sur base d’un jeu de données recueillies auprès de quelques banques de la ville province de Kinshasa, et un accent particulier sera mis sur les indices généraux de prix à la consommation. Il importe de noter que la prédiction dont il est questions ici, va jusqu’à la précision de manière claire des causes de l’inflation encours d’analyse, ou prédite, bien sur base des différentes variations des indicateurs. L’apprentissage automatique ici utilisée, nous offre plusieurs possibilités en terme d’algorithmes et modelés, mais dans le cadre de cet travail, nous n’allons aborder que quelques-uns, notamment la Régression Linéaire, l’algorithme de Forêt Aléatoire de Régression ou Radom Forest Régression, et l’arbre de décision modèle de Régression, desquels nous allons ressortir le meilleur algorithme au regard de son score.
Author Keywords: Apprentissage automatique, Inflation monétaire et Intelligence Artificielle.
How to Cite this Article
KABEYA TSHISEBA Cedric, Christ TSUNGU MIJIMBU, Glad LUFIMPU, and Christian MUABI KATENDA, “Linear regression and prediction of monetary inflation: Application for the Central Bank of Congo (DRC),” International Journal of Innovation and Applied Studies, vol. 40, no. 3, pp. 573–581, September 2023.
