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GM (1,1) ve EXGM (1,1) Tahmin Modellerinin Türkiye’nin Ar-Ge Harcamalarına Uygulanması

Year 2022, Volume: 6 Issue: 2, 95 - 106, 30.12.2022

Levent Akyüz , Halis Bilgil

https://doi.org/10.29002/asujse.1087288

Abstract

Disiplinler arası bir bilim alanı olan Gri sistem Teorisi az sayıda veri kullanarak bilinmeyen sistemlerin davranışını tahmin etmektedir ve birçok alana başarı ile uygulanmıştır. Bu çalışmada ise Gri Sistem Teorisi Türkiye’nin Araştırma ve Geliştirme (Ar-Ge) harcamalarını tahmin etmek için kullanılmıştır. 2011-2020 yılları arasındaki Ar-Ge harcamaları verileri Türkiye İstatistik Kurumu’ndan alınarak 2021-2030 yıllarının tahmini Standart Gri Model (GM (1,1)) ve Üstel Gri Model (EXGM (1,1)) kullanılarak yapılmıştır. Her iki modelin tahmin değerlerindeki hata ve korelasyon sonuçları karşılaştırılmıştır. 2011-2020 yılları arasındaki verileri kullanarak, gerçek ve tahmin değeri arasındaki yüzde bağıl hata (RPE) ve ortalama yüzde bağıl hata (MAPE) belirlenmiştir.
Sonuç olarak MAPE değeri GM (1,1) modeli ile %2.95 ve EXGM (1,1) modeli ile %1.48 olarak hesaplanmıştır. Gerçek değer ve tahmini değerin arasındaki korelasyonu gösteren R2 değerleri sıra ile GM(1,1) modeli için 0.9974 ve EXGM (1,1) modeli için 0.9985 olarak belirlenmiştir. Hesaplanan bağıl hata ve R2 değerleri ile tahminin kesinliği ortaya konmuştur. Buna göre 2021-2030 yılları için Ar-Ge harcamaları tahmin edilmiştir. EXGM (1,1) yönteminin tahmin kesinliğinin daha yüksek olduğu belirlenmiştir. Sonuç olarak önümüzdeki on yıl içerisinde Ülkemizin Ar-Ge harcamalarının üstel bir şekilde artacağı öngörülmektedir.

Keywords

Gri Tahmin Modeli Gri Sistem Teorisi Tahmin Doğruluğu En Küçük Kareler Yöntemi

References

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  • [4] J. Abdi, B. Moshiri, B. Abdulhai, A.K. Sedigh, Engineering Applications of Artificial Intelligence, 25 (5) (2012) 1022-1042.
  • [5] J. Wang, Q. Shi, Transportation Research Part C: Emerging Technologies, 27 (2013) 219-232.
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  • [8] M. Jin, X. Zhou, Z.M. Zhang, M.M. Tentzeris, Expert Systems with Applications, 39 (1) (2012) 773-779.
  • [9] J. Deng, Grey prediction and decision, (Huazhong University of Science & Technology Press, Wuhan, 2002) pp 1-2.
  • [10] Y. Kedong, G. Yan, L. Xuemei, Journal of Systems Engineering and Electronics, 29 (3) (2018) 560-570.
  • [11] L. Wu, S. Liu, D. Chen, L. Yao, W. Cui, Natural Hazards, 71 (3) (2014) 2231-2236.
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  • [15] S.-L. Ou, Computers and Electronics in Agriculture, 85 (2012) 33-39.
  • [16] B. Zeng, Y. Tan, H. Xu, J. Quan, L. Wang, X. Zhou, Journal of Grey System, 30 (1) (2018) 159-174.
  • [17] S. Ene, N. Öztürk, Technological Forecasting and Social Change, 115 (2017) 155-166.
  • [18] U. Şahin, T. Şahin, Chaos, Solitons & Fractals, 138 (2020) 109948-109955.
  • [19] J. Guo, W. Liu, L. Tu, Y. Chen, Energy Reports, 7 (2021) 7238-7250.
  • [20] H. Bilgil, Z. Ozturk, E. Ozgul, Aksaray University Journal of Science and Engineering, 3 (2) (2019) 75-81.
  • [21] D. Akay, M. Atak, Energy, 32 (9) (2007) 1670-1675.
  • [22] J.-Y. Chiang, International Journal of Heat and Mass Transfer, 51 (3-4) (2008) 576-585.
  • [23] X. Ma, Z. Liu, Journal of Computational and Applied Mathematics, 324 (2017) 17-24.

Application of GM (1,1) and EXGM (1,1) Forecasting Models to Turkey's Research and Development Expenditures

Year 2022, Volume: 6 Issue: 2, 95 - 106, 30.12.2022

Levent Akyüz , Halis Bilgil

https://doi.org/10.29002/asujse.1087288

Abstract

Grey system theory, which is an interdisciplinary field of science, predicts the behaviour of unknown systems using a small amount of data and has been successfully applied to many fields. In this study, the Grey System Theory is used to estimate Turkey's Research and Development (R&D) expenditures. Turkey's R&D expenditure datas were taken from the Turkish Statistical Institute for the years 2011-2020 and estimated for the years 2021-2030 using Standard Gray Model (GM (1,1)) and Exponential Gray Model (EXGM (1,1)). The error and correlation results in the prediction values of both models were compared. The percentage relative error (RPE) between the actual and predicted values and mean absolute percentage error (MAPE) were determined using the data from 2011-2020.
As a result, the MAPE value was calculated as 2.95 % with GM (1,1) model and 1.48 % with EXGM (1,1) model. R2 values showing the correlation between the actual and predicted values were determined as 0.9974 for GM (1,1) and 0.9985 for EXGM (1,1), respectively. The precision of the estimation was demonstrated with the calculated errors and R2 values. It has been determined that the estimation precision of the EXGM (1,1) method is higher. Accordingly, R&D expenditures were estimated for the years 2021-2030. As a result, it is predicted that Turkey's R&D expenditures will increase exponentially in the next ten years.

Keywords

Grey Estimation Model Grey System Theory Forecasting Accuracy Least Squares Method.

References

  • [1] E. Kayacan, B. Ulutas, O. Kaynak, Expert Systems with Applications, 37 (2) (2010) 1784-1789.
  • [2] K.Y. Huang, C.-J. Jane, Expert Systems with Applications, 36 (3) (2009) 5387-5392.
  • [3] S.-C. Huang, P.-J. Chuang, C.-F. Wu, H.-J. Lai, Expert Systems with Applications, 37 (12) (2010) 8590-8598.
  • [4] J. Abdi, B. Moshiri, B. Abdulhai, A.K. Sedigh, Engineering Applications of Artificial Intelligence, 25 (5) (2012) 1022-1042.
  • [5] J. Wang, Q. Shi, Transportation Research Part C: Emerging Technologies, 27 (2013) 219-232.
  • [6] C. Antoniou, H.N. Koutsopoulos, G. Yannis, Transportation Research Part C: Emerging Technologies, 34 (2013) 89-107.
  • [7] X. An, D. Jiang, C. Liu, M. Zhao, Expert Systems with Applications, 38 (9) (2011) 11280-11285.
  • [8] M. Jin, X. Zhou, Z.M. Zhang, M.M. Tentzeris, Expert Systems with Applications, 39 (1) (2012) 773-779.
  • [9] J. Deng, Grey prediction and decision, (Huazhong University of Science & Technology Press, Wuhan, 2002) pp 1-2.
  • [10] Y. Kedong, G. Yan, L. Xuemei, Journal of Systems Engineering and Electronics, 29 (3) (2018) 560-570.
  • [11] L. Wu, S. Liu, D. Chen, L. Yao, W. Cui, Natural Hazards, 71 (3) (2014) 2231-2236.
  • [12] Y. Zhang, Y. Xu, Z. Wang, Chaos, Solitons & Fractals, 42 (2) (2009) 1003-1009.
  • [13] W. Zhou, J.-M. He, Applied Mathematical Modelling, 37 (9) (2013) 6234-6243.
  • [14] S.A. Javed, S. Liu, Scientometrics, 115 (1) (2018) 395-413.
  • [15] S.-L. Ou, Computers and Electronics in Agriculture, 85 (2012) 33-39.
  • [16] B. Zeng, Y. Tan, H. Xu, J. Quan, L. Wang, X. Zhou, Journal of Grey System, 30 (1) (2018) 159-174.
  • [17] S. Ene, N. Öztürk, Technological Forecasting and Social Change, 115 (2017) 155-166.
  • [18] U. Şahin, T. Şahin, Chaos, Solitons & Fractals, 138 (2020) 109948-109955.
  • [19] J. Guo, W. Liu, L. Tu, Y. Chen, Energy Reports, 7 (2021) 7238-7250.
  • [20] H. Bilgil, Z. Ozturk, E. Ozgul, Aksaray University Journal of Science and Engineering, 3 (2) (2019) 75-81.
  • [21] D. Akay, M. Atak, Energy, 32 (9) (2007) 1670-1675.
  • [22] J.-Y. Chiang, International Journal of Heat and Mass Transfer, 51 (3-4) (2008) 576-585.
  • [23] X. Ma, Z. Liu, Journal of Computational and Applied Mathematics, 324 (2017) 17-24.
There are 23 citations in total.

Details

Primary Language Turkish
Journal Section Research Article
Authors

Levent Akyüz 0000-0002-8458-7042

Halis Bilgil 0000-0002-8329-5806

Publication Date December 30, 2022
Submission Date March 14, 2022
Acceptance Date July 18, 2022
Published in Issue Year 2022Volume: 6 Issue: 2

Cite

APA Akyüz, L., & Bilgil, H. (2022). GM (1,1) ve EXGM (1,1) Tahmin Modellerinin Türkiye’nin Ar-Ge Harcamalarına Uygulanması. Aksaray University Journal of Science and Engineering, 6(2), 95-106. https://doi.org/10.29002/asujse.1087288
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