ENHANCING WEIGHTED FUZZY TIME SERIES FORECASTING THROUGH PARTICLE SWARM OPTIMIZATION
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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References
Z. Liu, Z. Zhu, J. Gao, and C. Xu, “Forecast Methods for Time Series Data: A Survey,” IEEE Access, vol. 9, pp. 91896–91912, 2021, doi: 10.1109/ACCESS.2021.3091162.
B. Kumar Jha and S. Pande, “Time Series Forecasting Model for Supermarket Sales using FB-Prophet,” in Proceedings - 5th International Conference on Computing Methodologies and Communication, ICCMC 2021, Institute of Electrical and Electronics Engineers Inc., Apr. 2021, pp. 547–554. doi: 10.1109/ICCMC51019.2021.9418033.
A. J. F. Zamelina, D. Adytia, and A. W. Ramadhan, “Forecasting of Maximum Temperature by using ANFIS and GRU Algorithms: Case Study in Jakarta, Indonesia,” in 2022 10th International Conference on Information and Communication Technology, ICoICT 2022, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 222–227. doi: 10.1109/ICoICT55009.2022.9914885.
T. Novianti, I. D. Utami, and W. A. P. Dania, “Forecasting of salt demand using ARIMA model to prevent the bullwhip effect in salt supply chain,” in Journal of Physics: Conference Series, Institute of Physics, Feb. 2022. doi: 10.1088/1742-6596/2193/1/012035.
U. M. Sirisha, M. C. Belavagi, and G. Attigeri, “Profit Prediction Using ARIMA, SARIMA and LSTM Models in Time Series Forecasting: A Comparison,” IEEE Access, vol. 10, pp. 124715–124727, 2022, doi: 10.1109/ACCESS.2022.3224938.
S. Wang, C. Li, and A. Lim, “Why Are the ARIMA and SARIMA not Sufficient,” Apr. 2019, [Online]. Available: http://arxiv.org/abs/1904.07632
Q. Song and B. S. Chissom, “Fuzzy time series and its models,” Fuzzy Sets Syst, vol. 54, no. 3, pp. 269–277, Mar. 1993, doi: 10.1016/0165-0114(93)90372-O.
F. Alpaslan et al., “A Novel Seasonal Fuzzy Time series Method,” Hacettepe Journal of Mathematics and Statistics, vol. 41, no. 3, pp. 375-385, 2012.
T. Vovan, L. Nguyenhuynh, and T. Lethithu, “A forecasting model for time series based on improvements from fuzzy clustering problem,” Ann Oper Res, vol. 312, no. 1, pp. 473–493, May 2022, doi: 10.1007/s10479-021-04041-z.
H. K. Yu, “Weighted fuzzy time series models for TAIEX forecasting,” Physica A: Statistical Mechanics and its Applications, vol. 349, no. 3–4, pp. 609–624, Apr. 2005, doi: 10.1016/j.physa.2004.11.006.
S. Surono and N. Siregar, “The New Approach Optimization Markov Weighted Fuzzy Time Series using Particle Swarm Algorithm,” Journal of Education and Science, vol. 31, no. 2, pp. 42–54, Jun. 2022, doi: 10.33899/edusj.2022.133052.1217.
N. Van Tinh, N. C. Dieu, N. T. Duy, and T. T. Thanh, “Improved Fuzzy Time Series Forecasting Model Based on Optimal Lengths of Intervals Using Hedge Algebras and Particle Swarm Optimization,” Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 1286–1297, Feb. 2021, doi: 10.25046/aj0601147.
J. Calleja-Agius, K. England, and N. Calleja, “The effect of global warming on mortality,” Early Hum Dev, vol. 155, Apr. 2021, doi: 10.1016/j.earlhumdev.2020.105222.
T. Suratno, E. Saputra, Z. Abidin, D. Arsa, and N. Syarief, “Internet of Things (IoT) Arduino-Based Classroom Monitoring Utilizes Temperature Sensors And CO2 Sensors,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 16, no. 3, p. 313, Jul. 2022, doi: 10.22146/ijccs.76241.
J. P. Musashi, H. Pramoedyo, and R. Fitriani, “Comparison of Inverse Distance Weighted and Natural Neighbor Interpolation Method at Air Temperature Data in Malang Region,” CAUCHY: Jurnal Matematika Murni dan Aplikasi, vol. 5, no. 2, pp. 48–54, May 2018, doi: 10.18860/ca.v5i2.4722.
M. Nur Auliya and A. Mulya, “Hail Identification Based on Weather Factor Analysis and Himawari 8 Satellite Imagery (Case Study of Hail On 2nd March 2021 in Malang Indonesia),” International Journal of Remote Sensing and Earth Sciences, vol. 18, no. 2, pp. 217–228, 2021, doi: 10.30536/j.ijreses.2021.v18.a3712.
Perdinan, R. F. Adi, and E. Y. Arini, “Regional Analysis of Prone Drought Areas under Future Climate Change Scenarios: Case Study Agropolitan of Malang District,” in IOP Conference Series: Earth and Environmental Science, Institute of Physics Publishing, Apr. 2017. doi: 10.1088/1755-1315/58/1/012024.
Suhartono, M. Hisyam Lee, and H. Javedani, “A Weighted Fuzzy Integrated Time Series for Forecasting Tourist Arrivals,” 2011.
K. Hussain, M. N. Mohd Salleh, S. Cheng, and Y. Shi, “Metaheuristic research: a comprehensive survey,” Artif Intell Rev, vol. 52, no. 4, pp. 2191–2233, Dec. 2019, doi: 10.1007/s10462-017-9605-z.
S. A. W. Dinata, A. A. Purbosari, and P. Hasanah, “Forecasting Indonesian Oil, Non-Oil and Gas Import Export with Fuzzy Time Series,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 16, no. 4, p. 389, Oct. 2022, doi: 10.22146/ijccs.78399.
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