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A Novel Fréchet Distribution for Inflation Rate Modeling and Comparative Machine Learning Forecasting

Author

Listed:
  • Zahrah Fayez Althobaiti
  • Abdulrahman M. A. Aldawsari
  • Pitchaya Wiratchotisatian
  • Aliyu Ismail Ishaq
  • Ahmad Abubakar Suleiman

Abstract

The complexity of inflation rate fluctuations poses a significant challenge to traditional statistical models, requiring the development of more dependable and adaptable methods. The primary objectives of this paper are to predict and model inflation rate data. We propose the novel Fréchet (NF) via the logarithmic transformation approach from the conventional Fréchet distribution. Its density function might be nearly symmetric, bimodal, right‐skewed, or left‐skewed. The hazard function of the NF distribution is highly flexible, capable of increasing, decreasing, being upside‐down bathtub‐shaped, or increasing‐decreasing, which is not possible with the traditional Fréchet distribution. We derive key statistical features of this distribution and obtain parameter estimates using various estimation methods. Monte Carlo simulations are used to demonstrate the accuracy of the parameter estimates. The potential of the NF distribution is empirically validated using monthly inflation rate data. Additionally, we conduct a comparative analysis of various time series approaches using statistical methods as well as machine learning models for predicting inflation rates, including ARIMA, recurrent neural networks (RNN), and support vector regression (SVR). The findings reveal that SVR outperforms other methods by achieving the lowest errors across all metrics, with a root mean squared error (RMSE) of 0.2225, a mean absolute error (MAE) of 0.1394, and a mean absolute percentage error (MAPE) of 0.020101, underscoring its effectiveness in modeling and predicting inflation rate data.

Suggested Citation

  • Zahrah Fayez Althobaiti & Abdulrahman M. A. Aldawsari & Pitchaya Wiratchotisatian & Aliyu Ismail Ishaq & Ahmad Abubakar Suleiman, 2025. "A Novel Fréchet Distribution for Inflation Rate Modeling and Comparative Machine Learning Forecasting," Journal of Mathematics, John Wiley & Sons, vol. 2025(1).
    • Handle: RePEc:wly:jjmath:v:2025:y:2025:i:1:n:5570060
    DOI: 10.1155/jom/5570060
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    References listed on IDEAS

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    1. Xingyu Zhang & Yuanyuan Liu & Min Yang & Tao Zhang & Alistair A Young & Xiaosong Li, 2013. "Comparative Study of Four Time Series Methods in Forecasting Typhoid Fever Incidence in China," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-11, May.
    2. Mukhtar M. Salah & M. El-Morshedy & M. S. Eliwa & Haitham M. Yousof, 2020. "Expanded Fréchet Model: Mathematical Properties, Copula, Different Estimation Methods, Applications and Validation Testing," Mathematics, MDPI, vol. 8(11), pages 1-29, November.
    3. Suleman Nasiru, 2018. "Extended Odd Fréchet-G Family of Distributions," Journal of Probability and Statistics, Hindawi, vol. 2018, pages 1-12, December.
    4. Musa Nakorji & Umaru Aminu, 2022. "Forecasting inflation using machine learning techniques," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 14(1), pages 45-55, June.
    5. Marcelo C. Medeiros & Gabriel F. R. Vasconcelos & Álvaro Veiga & Eduardo Zilberman, 2021. "Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 98-119, January.
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