Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Articles

COMPARISON OF ARIMA, EXPONENTIAL SMOOTHING, AND CHEN-SINGH FUZZY MODELS FOR INFLATION FORECASTING IN ASEAN COUNTRIES

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  • Tri Wijayanti Septiarini,
  • Selly Anastassia Amellia Kharis,
  • Anuraga Jayanegara,
  • Sahidan Abdulmana
Tri Wijayanti Septiarini
Mathematics Study Program, Faculty of Science and Technology, Universitas Terbuka, Indonesia
Selly Anastassia Amellia Kharis
Mathematics Study Program, Faculty of Science and Technology, Universitas Terbuka, Indonesia
Anuraga Jayanegara
Directorate for Strategic and Academic Reputation, IPB University, Indonesia
Sahidan Abdulmana
Department of Data Science and Analytics, Faculty of Science and Technology, Fatoni University, Thailand
DOI: https://doi.org/10.30598/barekengvol20iss1pp0619-0636
Published November 24, 2025
Keywords
  • ASEAN,
  • Forecasting,
  • Fuzzy,
  • Inflation,
  • Statistics
How to Cite
[1]
T. W. Septiarini, S. A. A. Kharis, A. Jayanegara, and S. Abdulmana, “COMPARISON OF ARIMA, EXPONENTIAL SMOOTHING, AND CHEN-SINGH FUZZY MODELS FOR INFLATION FORECASTING IN ASEAN COUNTRIES”, BAREKENG: J. Math. & App., vol. 20, no. 1, pp. 0619-0636, Nov. 2025.

Copyright (c) 2025 Tri Wijayanti Septiarini, Selly Anastassia Amellia Kharis, Anuraga Jayanegara, Sahidan Abdulmana

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Abstract

This study aims to (i) develop predictive models using statistical and fuzzy approaches, and (ii) evaluate their forecasting performance. The data were obtained from www.investing.com for the period 1961 to 2017 and focus on five ASEAN countries: Indonesia, Malaysia, the Philippines, Singapore, and Thailand. The statistical models used are Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing, while the fuzzy approaches include Chen and Singh fuzzy time series models. The dataset was divided into training and test sets in a 75%-25% proportion. ARIMA models capture trends and autocorrelations in time series data, while Exponential Smoothing uses exponentially weighted averages. Fuzzy models are designed to handle uncertainty and linguistic patterns in data. The results show that Singh’s fuzzy model yields the lowest error for Indonesia, while exponential smoothing and Chen fuzzy time series model demonstrate the same lowest error for Malaysia. For the Philippines, exponential smoothing is most accurate, whereas ARIMA and Singh fuzzy time series achieve the smallest error for Singapore. For Thailand, exponential smoothing and ARIMA perform equally well. However, the robustness of the forecasting model cannot be determined from either statistical or fuzzy methods, highlighting the challenge in determining the most robust model for inflation in the ASEAN region. The 75%-25% data split may also limit the generalizability of the findings. This study contributes a rare cross-country comparison of statistical and fuzzy forecasting methods in the ASEAN context. It highlights the importance of model selection based on country-specific inflation behavior and provides insights for improving forecasting strategies in macroeconomic applications.

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