ENHANCING WEIGHTED FUZZY TIME SERIES FORECASTING THROUGH PARTICLE SWARM OPTIMIZATION

  • Armando Jacquis Federal Zamelina Study Program Master of Statistics, Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya, Indonesia https://orcid.org/0000-0002-9909-6860
  • Suci Astutik Study Program Master of Statistics, Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya, Indonesia
  • Rahma Fitriani Study Program Master of Statistics, Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya, Indonesia
  • Adji Achmad Rinaldo Fernandes Study Program Master of Statistics, Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya, Indonesia
  • Lucius Ramifidisoa Department of Mathematical Informatics, Ecole Normale Supérieure pour l’Enseignement Technique (ENSET), University of Antsiranana, Madagascar
Keywords: Air Temperature, Forecasting, Order length, Particle Swarm Optimization, Weighted Fuzzy, Time Series

Abstract

Climate change is a complex process that has far-reaching consequences for daily living. Temperature is one of the climatic features. Knowing its future value through a forecasting model is critical, as it aids in earlier strategic decision-making. Without considering spatial factors, this study investigates an Air Temperature variable forecasting. Weighted Fuzzy Time Series (WFTS) is one of the forecasting techniques. Furthermore, the length of the interval and the extent to which previous values (Order length) are utilized in predicting the subsequent value are pivotal factors in WFTS modelization and its forecasting accuracy. Therefore, this research investigates the interval length and the Order length of the WFTS through the Particle Swarm Optimization (PSO) approach. The variable used is the air temperature in Malang, Indonesia. The dataset is taken from BMKG-Indonesia. The forecasting performance of classical WFTS is enhanced by setting an appropriate order level and employing Particle Swarm Optimization (PSO) to determine the optimal interval fuzzy length. As indicated by the Evaluation matrices in the result section, the proposed optimization overtaken the classical WFTS in term of accuracy. The evaluation indicates a Mean Absolute Percentage Error (MAPE) value of 1.25 and a Root Mean Square Error (RMSE) of 0.32 for the Proposed model. In contrast, the classical WFTS demonstrates a MAPE of 2.26 and RMSE of 0.58. The implementation of the PSO provides solid insights for Air temperature forecasting accuracy.

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Published
2024-10-14
How to Cite
[1]
A. Zamelina, S. Astutik, R. Fitriani, A. Fernandes, and L. Ramifidisoa, “ENHANCING WEIGHTED FUZZY TIME SERIES FORECASTING THROUGH PARTICLE SWARM OPTIMIZATION”, BAREKENG: J. Math. & App., vol. 18, no. 4, pp. 2675-2684, Oct. 2024.