Forecasting the gross domestic product using a weight direct determination neural network

Spyridon D. Mourtas; Emmanouil Drakonakis; Zacharias Bragoudakis; Spyridon D. Mourtas; Emmanouil Drakonakis; Zacharias Bragoudakis

doi:10.3934/math.20231237

AIMS Mathematics

2023, Volume 8, Issue 10: 24254-24273. doi: 10.3934/math.20231237

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Forecasting the gross domestic product using a weight direct determination neural network

1.
Department of Economics, Mathematics-Informatics and Statistics-Econometrics, National and Kapodistrian University of Athens, Sofokleous 1 Street, 10559 Athens, Greece
2.
Laboratory "Hybrid Methods of Modelling and Optimization in Complex Systems", Siberian Federal University, Prosp. Svobodny 79, 660041 Krasnoyarsk, Russia
3.
Bank of Greece, 10250 Athens, Greece
4.
National and Kapodistrian University of Athens, Greece

Received: 01 July 2023 Revised: 21 July 2023 Accepted: 28 July 2023 Published: 14 August 2023
MSC : 68T10, 65F20, 91B40

One of the most often used data science techniques in business, finance, supply chain management, production, and inventory planning is time-series forecasting. Due to the dearth of studies in the literature that propose unique weights and structure (WASD) based models for regression issues, the goal of this research is to examine the creation of such a model for time-series forecasting. Given that WASD neural networks have been shown to overcome limitations of traditional back-propagation neural networks, including slow training speed and local minima, a multi-function activated WASD for time-series (MWASDT) model that uses numerous activation functions, a new auto cross-validation method and a new prediction mechanism are proposed. The MWASDT model was used in forecasting the gross domestic product (GDP) for numerous nations to show off its exceptional capacity for learning and predicting. Compared to previous WASD-based models for time-series forecasting and traditional machine learning models that MATLAB has to offer, the new model has produced noticeably better forecasting results, especially on unseen data.
- neural networks,
- time-series forecasting,
- machine learning,
- gross domestic product,
- weights and structure determination
Citation: Spyridon D. Mourtas, Emmanouil Drakonakis, Zacharias Bragoudakis. Forecasting the gross domestic product using a weight direct determination neural network[J]. AIMS Mathematics, 2023, 8(10): 24254-24273. doi: 10.3934/math.20231237

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Abstract

One of the most often used data science techniques in business, finance, supply chain management, production, and inventory planning is time-series forecasting. Due to the dearth of studies in the literature that propose unique weights and structure (WASD) based models for regression issues, the goal of this research is to examine the creation of such a model for time-series forecasting. Given that WASD neural networks have been shown to overcome limitations of traditional back-propagation neural networks, including slow training speed and local minima, a multi-function activated WASD for time-series (MWASDT) model that uses numerous activation functions, a new auto cross-validation method and a new prediction mechanism are proposed. The MWASDT model was used in forecasting the gross domestic product (GDP) for numerous nations to show off its exceptional capacity for learning and predicting. Compared to previous WASD-based models for time-series forecasting and traditional machine learning models that MATLAB has to offer, the new model has produced noticeably better forecasting results, especially on unseen data.

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