SavedAdd your journal

Journals / Gazi University Journal of Science / 2026 / Volume: 38 Issue: 3

Forecasting the Consumer Price Index in Türkiye Using Machine Learning Models: A Comparative Analysis

Mehmet Eren Nalici(Abdullah Gül Üniversitesi)0000-0002-7954-6916
İsmet Soylemez(Abdullah Gül Üniversitesi)0000-0002-8253-9389
Ramazan Ünlü(ABDULLAH GÜL ÜNİVERSİTESİ)0000-0002-1201-195X
Pages
—
Publication Date
2025-09-01
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings indicate that while the RF model consistently generates the most accurate forecasts across all shifts, the SVM model demonstrates a particular strength in the domain of short-term predictions.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study utilizes machine learning models to forecast Türkiye's Consumer Price Index (CPI), thereby addressing a critical gap in inflation prediction methodologies.
📊
Key Findings & Data:The primary objective of this study is to evaluate the performance of three machine learning models, such as Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM), in forecasting CPI over periods ranging from one to six months, utilizing data from 2012 to 2024.
💡
Conclusion & Impact:The findings indicate that while the RF model consistently generates the most accurate forecasts across all shifts, the SVM model demonstrates a particular strength in the domain of short-term predictions.
🔬Design:Modeling & Simulation
📍Scope:2012 Verileri
⚙️Variables:Consumer Price Index · Forecasting · Economic growth

Abstract

This study utilizes machine learning models to forecast Türkiye's Consumer Price Index (CPI), thereby addressing a critical gap in inflation prediction methodologies. The central research problem involves the forecasting of CPI in a volatile economic environment, which is essential for informed policymaking. The primary objective of this study is to evaluate the performance of three machine learning models, such as Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM), in forecasting CPI over periods ranging from one to six months, utilizing data from 2012 to 2024. The study's unique contribution lies in the application of the "SelectKBest" method, which identifies the most relevant indices, thereby enhancing the efficiency of the models. An ensemble method, Averaging Voting, is also employed to combine the strengths of these models, producing more accurate and robust predictions. The findings indicate that while the RF model consistently generates the most accurate forecasts across all shifts, the SVM model demonstrates a particular strength in the domain of short-term predictions. The ensemble model demonstrates a substantial performance improvement, with a R2 value of 0.962 for one-month ahead of estimates and 0.956 for five-month forecasts. This combined approach has been shown to outperform individual models, offering a more reliable framework for CPI forecasting. The findings offer valuable insights for economic policymakers, enabling more precise and stable inflation predictions in Türkiye.

🔖

Cite This Article

Nalici, M. E., Soylemez, İ., Ünlü, R., *, İ. S. (2025). Forecasting the Consumer Price Index in Türkiye Using Machine Learning Models: A Comparative Analysis. Gazi University Journal of Science, Volume: 38 Issue: 3. https://doi.org/10.35378/gujs.1558496

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (52)

  1. Jain, M., Kostyshyna, O., and Zhang, X, “How do people view wage and price inflation?”, Journal of Monetary Economics, 145: 103552, (2024). DOI: https://doi.org/10.1016/J.Jmoneco.2024.01.005
  2. Zhu, P., Zhou, Q., and Zhang, Y., “Investor attention and consumer price index inflation rate: evidence from the United States”, Humanities and Social Sciences Communications, 11(1): 541, (2024). DOI: https://doi.org/10.1057/S41599-024-03036-Y
  3. Uçucu, A., Gök, B., and Gökçen, H., “Prediction of life quality index value rankings of countries after the covıd-19 pandemic by artificial neural networks”, Politeknik Dergisi, 27(2): 689–698, (2024). DOI: https://doi.org/10.2339/Politeknik.1113718
  4. Poudel, O., Kharel, K. R., Acharya, P., Simkhada, D., and Kafle, S. C., “Arima modeling and forecasting of national consumer price index in Nepal”, Interdisciplinary Journal of Management and Social Sciences, 5(1): 105–118, (2024). DOI: https://doi.org/10.3126/İjmss.V5i1.62666
  5. Investing.Com, “Investing.com.” Accessed: Jul. 04, 2024. [Online]. Available: https://tr.investing.com/
  6. Palumbo, L., and Laureti, T., “Finding the Goldilocks data collection frequency for the consumer price index”, SSRN Electronic Journal, (2024). DOI: https://doi.org/10.2139/ssrn.4832198
  7. Cui, Q., Rong, S., Zhang, F., Wang, X., “Exploring and predicting China’s consumer price index with its influence factors via big data analysis”, Journal of Intelligent & Fuzzy Systems, 46(1): 891-901, (2023). DOI: https://doi.org/10.3233/jifs-234102
  8. Shinkarenko, V., Hostryk, A., Shynkarenko, L., and Dolinskyi, L., “A forecasting the consumer price index using time series model”, Shs Web of Conferences, 107: 10002, (2021). DOI: https://doi.org/10.1051/Shsconf/202110710002
  9. Mwanga, Y., “Arima forecasting model for Uganda’s consumer price index”, American Journal of Theoretical and Applied Statistics, 9(5): 238-244, (2020). DOI: https://doi.org/10.11648/J.Ajtas.20200905.17
  10. Nyoni, T., “Arima modeling and forecasting of consumer price index (CPI) in Germany”, University Library of Munich, (2019).
  11. Hu, Q., “Forecasting analysis of the Hungarian CPI based on the arima model”, Highlights in Business, Economics and Management, 28: 240–245, (2024). DOI: https://doi.org/10.54097/ybwgbd39
  12. Ibrahim, A., Sani, U. M., and Olokojo, V. O., “Forecastıng consumer price index and exchange rate using arima models: empirical evidence from Nigeria”, Fudma Journal of Sciences, 6(6): 114–124, (2023). DOI: https://doi.org/10.33003/Fjs-2022-0606-1136
  13. Deka, A. and Resatoglu, N. G., “Forecasting foreign exchange rate and consumer price index with arima model: the case of Turkey”, International Journal of Scientific Research and Management, 7(8): 1254-1275, (2019). DOI: https://doi.org/https://doi.org/10.18535/ijsrm/v7i8.em01
  14. Radzi, H. M. and Abu Bakar, A. S., “Forecasting GDP and CPI of the Malaysian economy using arıma models”, 8th Annual Ecofı Symposium 2022, (2022).
  15. Boniface, A. and Martin, A., “Time series modeling and forecasting of consumer price index in Ghana”, Journal of Advances in Mathematics and Computer Science, 32(1): 1–11, (2019). DOI: https://doi.org/10.9734/Jamcs/2019/V32i130134
  16. Norbert, H., “Modeling and forecasting consumer price index (case of Rwanda)”, American Journal of Theoretical and Applied Statistics, 5(3): 101-107, (2016). DOI: https://doi.org/10.11648/J.Ajtas.20160503.14
  17. Mia, M. S., Shahajada Mia, M., Musfiqur, M., Nabeen, R., and Masrufa Akter, M., “Modelling and forecasting the consumer price index in Bangladesh through econometric models”, American Scientific Research Journal for Engineering, Technology, and Sciences, 59(1): 118–127, (2019).
  18. Mohamed, J., “Time series modeling and forecasting of Somaliland consumer price index: a comparison of arima and regression with arima errors”, American Journal of Theoretical and Applied Statistics, 9(4): 143-153, (2020). DOI: https://doi.org/10.11648/J.Ajtas.20200904.18
  19. Nyoni, T., “Time series modeling and forecasting of the consumer price index in Belgium”, Mpra Paper, University Library of Munich Germany, (2019).
  20. Purbasari, I. Y., Anggraeny, F. T., and Ardiningrum, N. A., “Time-series modeling for consumer price index forecasting using comparison analysis of autoregressive integrated moving average and artificial neural network”, Scitepress, 599–604, (2021). DOI: https://doi.org/10.5220/0010369205990604
  21. Weng, D., “The consumer price index forecast based on arima model”, in Proceedings - 2010 Wase International Conference on Information Engineering, 307–310, (2010). DOI: https://doi.org/10.1109/Icıe.2010.79
  22. Asati, A., “A comparative study on forecasting consumer price index of India amongst xgboost, theta, arima, prophet and LSTM algorithms”, Osf Preprint, Center for Open Science, (2022).
  23. Sibai, F. N., El-Moursy, A., and Sibai, A., “Forecasting the consumer price index: a comparative study of machine learning methods”, International Journal of Computing and Digital Systems, 15(1): 487–497, (2024). DOI: https://doi.org/10.12785/İjcds/150137
  24. Joseph, A., Potjagailo, G., Chakraborty, C., and Kapetanios, G., “Forecasting UK inflation bottom up”, International Journal of Forecasting, 40(4): 1521-1538, (2024). DOI: https://doi.org/10.1016/J.İjforecast.2024.01.001
  25. Qin, X., Sun, M., Dong, X., and Zhang, Y., “Forecasting of China consumer price index based on EEMD and SVR method”, in Proceedings - 2nd International Conference on Data Science and Business Analytics, Icdsba 2018, Institute of Electrical and Electronics Engineers Inc., 329–333, (2018). DOI: https://doi.org/10.1109/Icdsba.2018.00069
  26. Riofrio, J., Infante, S., and Hernández, A., “Forecasting the consumer price index of Ecuador using classical and advanced time series models”, in Communications in Computer and Information Science, Springer Science and Business Media Deutschland Gmbh, 1885: 128–144, (2023). DOI: https://doi.org/10.1007/978-3-031-45438-7_9
  27. Nguyen, T. T., Nguyen, H. G., Lee, J. Y., Wang, Y. L., and Tsai, C. S., “The consumer price index prediction using machine learning approaches: evidence from the United States”, Heliyon, 9(10), (2023). DOI: https://doi.org/10.1016/J.Heliyon.2023.E20730
  28. Cobbinah, M. and Alnaggar, A., “An attention encoder-decoder RNN model with teacher forcing for predicting consumer price index”, Journal of Data, Information and Management, 6(1): 65–83, (2024). DOI: https://doi.org/10.1007/S42488-024-00114-3
  29. Álvarez-Díaz, M. and Gupta, R., “Forecasting us consumer price index: does nonlinearity matter?”, Applied Economics, 48(46): 4462–4475, (2016). DOI: https://doi.org/10.1080/00036846.2016.1158922
  30. Njenga, J. K., “Analysis and forecasting of consumer price index (CPI) in Kenya and South Africa using holt winter model”, Asian Journal of Economics, Business and Accounting, 24(4): 322–331, (2024). DOI: https://doi.org/10.9734/ajeba/2024/v24i41283
  31. Ali, A. and Mahgoub, A., “Prediction of CPI in Saudi Arabia: holt’s linear trend approach”, Research in World Economy, 11(6): 302-310, (2020). DOI: https://doi.org/10.5430/Rwe.V11n6p302
  32. Permatasari, N. P., Chotimah, H., Permana, P., Tarigan, W. S., Toharudin, T., and Ruchjana, B. N., “Application of GSTARI (1,1,1) model for forecasting the consumer price index (CPI) in three cities in Central Java”, Jurnal Teori Dan Aplikasi Matematika, 6(1): 134-143, (2022). DOI: https://doi.org/10.31764/Jtam.V6i1.6114
  33. Aabeyir, B., “Forecasting the consumer price index of Ghana using exponential smoothing methods”, Mathematical Theory and Modeling, 9(3): 1-10, (2019). DOI: https://doi.org/10.7176/MTM/9-3-01
  34. Hassani, H. and Silva, E. S., “Forecasting UK consumer price inflation using inflation forecasts”, Research in Economics, 72(3): 367–378, (2018). DOI: https://doi.org/10.1016/J.Rie.2018.07.001
  35. Zahara, S. and Sugianto, S., “Multivariate time series forecasting based cloud computing for consumer price index using deep learning algorithms”, in 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, Isrıtı 2020, Institute of Electrical and Electronics Engineers Inc., 338–343. (2020). DOI: https://doi.org/10.1109/ISRITI51436.2020.9315465
  36. Febriyanti, S., Pradana, W. A., Muhammad, J. S., and Widodo, E., “Forecasting the consumer price index in Yogyakarta by using the double exponential smoothing method”, Parameter Journal of Statistics, 2(1): 1–7, (2021). DOI: https://doi.org/10.22487/27765660.2021.v2.i1.15641
  37. Gjika Dhamo, E., Puka, L., and Zaçaj, O., “Forecasting consumer price index (CPI) using time series models and multi regression models (Albania case study)”, Vilnius Gediminas Technical University, 466-472, (2018). DOI: https://doi.org/10.3846/Bm.2018.51
  38. Nalici, M. E., Söylemez, İ., and Ünlü, R., “Symbolic aggregate approximation-based clustering of monthly natural gas consumption”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 13(1): 307–313, (2024). DOI: https://doi.org/10.17798/Bitlisfen.1395411
  39. Nalici, M. E., Söylemez, I., and Ünlü, R. “Strategic investment in bist100: a machine learning approach using symbolic aggregate approximation clustering”, International Journal of Industrial Engineering: Theory, Applications and Practice, 32(2): 382-395, (2025). DOI: https://doi.org/10.23055/ijietap.2025.32.2.10273
  40. Doğan, A., Özcan, U., and Söylemez, İ., “Trafik kazalarında birliktelik kuralı analizi: ankara ili örneği”, Ege Akademik Bakis (Ege Academic Review), 16(Ozel): 11-20, (2016). DOI: https://doi.org/10.21121/eab.2016OZEL24423
  41. Nalici, M. E., Söylemez, İ., and Ünlü, R., “Türkiye ‘de tahıl üretiminin tahminlemesi: karşılaştırmalı analiz”, Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, 40(2): 288–303, (2024).
  42. Ünlü, R., “Classification of historical Anatolian coins with machine learning algorithms”, Alphanumeric Journal, 7(2): 275–288, (2019). DOI: https://doi.org/10.17093/Alphanumeric.620095
  43. Güler, M., Kabakçı, A., Koç, Ö., Eraslan, E., Derin, K. H., Güler, M., Ünlü, R., Türkan, Y. S., and Namlı, E. “Forecasting of the unemployment rate in Turkey: comparison of the machine learning models”, Sustainability, 16(15): 6509, (2024). DOI: https://doi.org/10.3390/su16156509
  44. Khalyasmaa, A. Eroshenko, S. A., Chakravarthy, T. P., Gasi, V. G., Bollu, S. K. Y., Caire, R., “Prediction of solar power generation based on random forest regressor model”, in 2019 International Multi-Conference on Engineering, Computer and Information Sciences (Sıbırcon), 780–785, (2019). DOI: https://doi.org/10.1109/SIBIRCON48586.2019.8958063
  45. Luo, H., Cheng, F., Yu, H., and Yi, Y., “Sdtr: soft decision tree regressor for tabular data”, Ieee Access, 9: 55999–56011, (2021). DOI: https://doi.org/10.1109/Access.2021.3070575
  46. Kavya, D, “Optimizing performance: selectkbest for efficient feature selection in machine learning”, Accessed: Jul. 04, 2024. [Online]. Available: Https://Medium.Com/@Kavya2099/Optimizing-Performance-Selectkbest-For-Efficient-Feature-Selection-İn-Machine-Learning-3b635905ed48
  47. Kavitha, S, Varuna, S, and Ramya, R, “A comparative analysis on linear regression and support vector regression”, in 2016 Online International Conference on Green Engineering and Technologies (Ic-Get), 1–5, (2016). DOI: https://doi.org/10.1109/Get.2016.7916627
  48. Feurer, M. and Hutter, F., “Hyperparameter optimization”, Automated Machine Learning, 3–33, (2019). DOI: https://doi.org/10.1007/978-3-030-05318-5_1
  49. Dietterich T. G., “Ensemble methods in machine learning”, Multiple Classifier Systems, 1–15, (2000). DOI: https://doi.org/10.1007/3-540-45014-9_1
  50. Breiman, L., “Bagging predictors”, Machine Learning, 24(2): 123–140, (1996). DOI: https://doi.org/10.1007/Bf00058655
  51. Freund, Y. and Schapire, R. E., “A decision-theoretic generalization of on-line learning and an application to boosting”, Journal of Computer System Sciences, 55(1): 119–139, (1997). DOI: https://doi.org/10.1006/Jcss.1997.1504
  52. An, K. and Meng, J., “Voting-averaged combination method for regressor ensemble”, Advanced Intelligent Computing Theories and Applications, 540–546, (2010). DOI: https://doi.org/10.1007/978-3-642-14922-1_67