Comparative Forecasting of Some Key Economic Indicators Using Artificial Neural Networks and Ordinary Differential Equations: A Case Study of the Turkish Economy
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 Ins...
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| Format: | Article |
| Language: | English |
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Çanakkale Onsekiz Mart University
2024-12-01
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| Series: | Journal of Advanced Research in Natural and Applied Sciences |
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| Online Access: | https://dergipark.org.tr/en/download/article-file/4259271 |
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| author | Bahatdin Daşbaşı Murat Taşyürek |
| author_facet | Bahatdin Daşbaşı Murat Taşyürek |
| author_sort | Bahatdin Daşbaşı |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5e083ee8474a4989bcd7aa96dac23e2d |
| institution | Kabale University |
| issn | 2757-5195 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Çanakkale Onsekiz Mart University |
| record_format | Article |
| series | Journal of Advanced Research in Natural and Applied Sciences |
| spelling | doaj-art-5e083ee8474a4989bcd7aa96dac23e2d2025-02-05T18:13:03ZengÇanakkale Onsekiz Mart UniversityJournal of Advanced Research in Natural and Applied Sciences2757-51952024-12-0110492293710.28979/jarnas.1560309453Comparative Forecasting of Some Key Economic Indicators Using Artificial Neural Networks and Ordinary Differential Equations: A Case Study of the Turkish EconomyBahatdin Daşbaşı0https://orcid.org/0000-0001-8201-7495Murat Taşyürek1https://orcid.org/0000-0001-5623-8577Kayseri UniversityKayseri UniversityThis 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.https://dergipark.org.tr/en/download/article-file/4259271turkish economyartificial neural networksordinary differential equationscomparative forecasting |
| spellingShingle | Bahatdin Daşbaşı Murat Taşyürek Comparative Forecasting of Some Key Economic Indicators Using Artificial Neural Networks and Ordinary Differential Equations: A Case Study of the Turkish Economy Journal of Advanced Research in Natural and Applied Sciences turkish economy artificial neural networks ordinary differential equations comparative forecasting |
| title | Comparative Forecasting of Some Key Economic Indicators Using Artificial Neural Networks and Ordinary Differential Equations: A Case Study of the Turkish Economy |
| title_full | Comparative Forecasting of Some Key Economic Indicators Using Artificial Neural Networks and Ordinary Differential Equations: A Case Study of the Turkish Economy |
| title_fullStr | Comparative Forecasting of Some Key Economic Indicators Using Artificial Neural Networks and Ordinary Differential Equations: A Case Study of the Turkish Economy |
| title_full_unstemmed | Comparative Forecasting of Some Key Economic Indicators Using Artificial Neural Networks and Ordinary Differential Equations: A Case Study of the Turkish Economy |
| title_short | Comparative Forecasting of Some Key Economic Indicators Using Artificial Neural Networks and Ordinary Differential Equations: A Case Study of the Turkish Economy |
| title_sort | comparative forecasting of some key economic indicators using artificial neural networks and ordinary differential equations a case study of the turkish economy |
| topic | turkish economy artificial neural networks ordinary differential equations comparative forecasting |
| url | https://dergipark.org.tr/en/download/article-file/4259271 |
| work_keys_str_mv | AT bahatdindasbası comparativeforecastingofsomekeyeconomicindicatorsusingartificialneuralnetworksandordinarydifferentialequationsacasestudyoftheturkisheconomy AT murattasyurek comparativeforecastingofsomekeyeconomicindicatorsusingartificialneuralnetworksandordinarydifferentialequationsacasestudyoftheturkisheconomy |