An Imprecise High-Order Fuzzy Time Series Model Forecasting the Stocks Traded Using Central-Log-Ratio Transformation of Compositional Data
Abstract
Malaysia, Indonesia, and Thailand countries have half of the trade in the group of the Association of Southeast Asian Nations (ASEAN) members. An agreement has increased the importance of trade coalition without the US dollar among them. To understand the pattern of the Stock Traded in these countries, this study developed ample fuzzy time series foretelling models. An innovative data transformation approach called compositional data is employed. The Fuzzy Time series models are implemented with different orders of fuzzy logical relationships. The Trapezoidal and Spline S-shaped membership functions are engaged in these models. The performance of forecasting is evaluated through the compositional root mean square error (CRMSE) and compositional mean absolute percentage error (CMAPE). The analysis of forecasted accuracy measurements showed that the Third-Order Fuzzy Time Series model with a Trapezoidal membership function outclassed other orders models. It is also observable that Thailand's stock traded values increased compared to Malaysia and Indonesia.
Full text article
References
C. Zhao, X. Yuan, J. Long, L. Jin, and B. Guan, “Financial indicators analysis using machine learning: Evidence from chinese stock market,” Finance Research Letters, vol. 58, p. 104590, 2023. https://doi.org/10.1016/j.frl.2023.104590.
G. Liu, K. Zhong, H. Li, T. Chen, and Y. Wang, “A state of art review on time series forecasting with machine learning for environmental parameters in agricultural greenhouses,” Information Processing in Agriculture, vol. 11, no. 2, pp. 143–162, 2024. https://doi.org/10.1016/j.inpa.2022.10.005.
H.-W. Chang, T. Chang, and M.-C. Wang, “Revisit the impact of exchange rate on stock market returns during the pandemic period,” The North American Journal of Economics and Finance, vol. 70, p. 102068, 2024. https://doi.org/10.1016/j.najef.2023.102068.
J. Aitchison, “The statistical analysis of compositional data,” Journal of the Royal Statistical Society: Series B (Methodological), vol. 44, no. 2, pp. 139–160, 1982. https://doi.org/10.1111/j.2517-6161.1982.tb01195.x.
L. A. Zadeh, “The concept of a linguistic variable and its application to approximate reasoning—i,” Information sciences, vol. 8, no. 3, pp. 199–249, 1975. https://doi.org/10.1016/0020-0255(75)90046-8.
S. J. Russell and P. Norvig, Artificial intelligence: a modern approach. pearson, 2016.
Q. Song and B. S. Chissom, “Forecasting enrollments with fuzzy time series—part i,” Fuzzy sets and systems, vol. 54, no. 1, pp. 1–9, 1993. https://doi.org/10.1016/0165-0114(93)90355-L.
S.-M. Chen, “Forecasting enrollments based on high-order fuzzy time series,” Cybernetics and Systems, vol. 33, no. 1, pp. 1–16, 2002. https://doi.org/10.1080/019697202753306479.
K. Huarng, “Effective lengths of intervals to improve forecasting in fuzzy time series,” Fuzzy sets and systems, vol. 123, no. 3, pp. 387–394, 2001. https://doi.org/10.1016/S0165-0114(00)00057-9.
J. Wang, H. Li, H. Yang, and Y. Wang, “Intelligent multivariable air-quality forecasting system based on feature selection and modified evolving interval type-2 quantum fuzzy neural network,” Environmental Pollution, vol. 274, p. 116429, 2021. https://doi.org/10.1016/j.envpol.2021.116429.
Y. Alyousifi, M. Othman, R. Sokkalingam, I. Faye, and P. C. Silva, “Predicting daily air pollution index based on fuzzy time series markov chain model,” Symmetry, vol. 12, no. 2, p. 293, 2020. https://doi.org/10.3390/sym12020293.
Y. Gu, Y. Zhao, J. Zhou, H. Li, and Y. Wang, “A fuzzy multiple linear regression model based on meteorological factors for air quality index forecast,” Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 10523–10547, 2021. https://doi.org/10.3233/JIFS-201222.
N. S. W. YUSOFF, “A new single linkage robust clustering outlier detection procedures for multivariate data,” Sains Malaysiana, vol. 52, no. 8, pp. 2431–2451, 2023. https://doi.org/10.17576/jsm-2023-5208-19.
X. Shi, J. Wang, and B. Zhang, “A fuzzy time series forecasting model with both accuracy and interpretability is used to forecast wind power,” Applied Energy, vol. 353, p. 122015, 2024. https://doi.org/10.1016/j.apenergy.2023.122015.
´A. L´opez-Oriona, P. D’Urso, J. A. Vilar, and B. Lafuente-Rego, “Quantile-based fuzzy c-means clustering of multivariate time series: Robust techniques,” International Journal of Approximate Reasoning, vol. 150, pp. 55–82, 2022. https://doi.org/10.1016/S0165-0114(00)00057-9.
T. Carvalho, M. Vellasco, and J. F. Amaral, “Automatic generation of fuzzy inference systems for multivariate time series forecasting,” Fuzzy Sets and Systems, vol. 470, p. 108657, 2023. https://doi.org/10.1016/j.fss.2023.108657.
D. Qin, Z. Peng, and L. Wu, “Deep attention fuzzy cognitive maps for interpretable multivariate time series prediction,” Knowledge-Based Systems, vol. 275, p. 110700, 2023. https://doi.org/10.1016/j.knosys.2023.110700.
Y. Wei, Z. Wang, H. Wang, and Y. Li, “Compositional data techniques for forecasting dynamic change in china’s energy consumption structure by 2020 and 2030,” Journal of Cleaner Production, vol. 284, p. 124702, 2021. https://doi.org/10.1016/j.jclepro.2020.124702.
V. Pawlowsky-Glahn and J. J. Egozcue, “Compositional data and their analysis: an introduction,” Geological Society, London, Special Publications, vol. 264, no. 1, pp. 1–10, 2006. https://doi.org/10.1144/gsl.sp.2006.264.01.01.
J. Palarea-Albaladejo, J. Mart´ın-Fern´andez, and J. Soto, “Dealing with distances and transformations for fuzzy c-means clustering of compositional data,” Journal of Classification, vol. 29, pp. 144–169, 2012. https://doi.org/10.1007/s00357-012-9105-4.
K. Kim, J. Park, and S. Jung, “Principal component analysis for zero-inflated compositional data,” Computational Statistics & Data Analysis, vol. 198, p. 107989, 2024. https://doi.org/10.1016/j.csda.2024.107989.
H. Huang, Y. Tian, and Z. Tao, “Multi-rule combination prediction of compositional data time series based on multivariate fuzzy time series model and its application,” Expert Systems with Applications, vol. 238, p. 121966, 2024. https://doi.org/10.1016/j.eswa.2023.121966.
T. W. Bank, “Stocks traded, total value (current us$) - indonesia, malaysia, thailand,” 2022. Accessed: 2024-08-15.
MathWorks, “S-shaped membership function (smf),” 2023. Accessed: 2024-08-15.
MathWorks, “Trapezoidal membership function (trapezoidalmf),” 2023. Accessed: 2024-08-15.
Authors
Copyright (c) 2025 Journal of the Indonesian Mathematical Society
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Article Details
