Comparative Forecasting of Some Key Economic Indicators Using Artificial Neural Networks and Ordinary Differential Equations: A Case Study of the Turkish Economy

Year 2024, Volume: 10 Issue: 4, 922 - 937, 31.12.2024

Bahatdin Daşbaşı , Murat Taşyürek

https://doi.org/10.28979/jarnas.1560309

Abstract

This study explores the relationships between the USD opening exchange rate, the annual change rate of the Consumer Price Index (CPI), the housing loan interest rate in Turkish lira, and the residential construction cost index from January 2015 to May 2024 using data from the Turkish Statistical Institute (TUIK). Artificial Neural Networks (ANN) and Ordinary Differential Equations (ODE) methods were employed to model the interactions among these four variables. In the ANN approach, each variable was modeled as the dependent variable in turn, with the remaining three serving as independent variables, resulting in four distinct analyses. The ODE model, on the other hand, provided a holistic analysis by capturing the time-dependent relationships among all four variables simultaneously. The ANN model predictions achieved accuracy rates of 87.2% for the USD opening exchange rate, 91.4% for the CPI annual change rate, 85.9% for the housing loan interest rate, and 93.1% for the construction cost index. Meanwhile, the ODE model demonstrated its strength by offering a more comprehensive framework with an overall accuracy of 94.6%, effectively capturing the complex interdependencies among the variables. These findings highlight the strengths of both approaches: while the ANN model excels in analyzing individual variables, the ODE model offers a broader perspective by integrating all variables into a unified framework. This study contributes to developing economic forecasting models and provides valuable insights for decision-makers, particularly in times of economic uncertainty.

Keywords

Turkish economy, artificial neural networks, ordinary differential equations, comparative forecasting

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