(Institut Agama Islam Negeri (IAIN) Tulungagung, Indonesia)(2) Wayan Firdaus Mahmudy
(Universitas Brawijaya, Indonesia)(3) * Aji Prasetya Wibawa
(Universitas Negeri Malang, Indonesia)*corresponding author
AbstractForecasting is a method of predicting past and current data, most often by pattern analysis. A Fuzzy Takagi Sugeno Kang (TSK) study can predict Indonesia's inflation rate, yet with too high error. This study proposes an accuracy improvement based on Evolution Strategies (ES), a specific evolutionary algorithm with good performance optimization problems. ES algorithm used to determine the best coefficient values on consequent fuzzy rules. This research uses Bank Indonesia time-series data as in the previous study. ES algorithm uses the popSize test to determine the number of initial chromosomes to produce the best optimal solution for this problem. The increase of popSize creates better fitness value due to the ES's broader search area. The RMSE of ES-TSK is 0.637, which outperforms the baseline approach. This research generally shows that ES may reduce repetitive experiment events due to Fuzzy coefficients' manual setting. The algorithm complexity may cost to the computing time, yet with higher performance.
KeywordsEvolution strategies; TSK fuzzy logic; Inflation rate; Forecasting; Mean Square Error
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DOIhttps://doi.org/10.26555/ijain.v7i1.376 |
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References
[1] B. Al-Shboul, H. Faris, and N. Ghatasheh, "Initializing Genetic Programming using fuzzy clustering and its application in churn prediction in the telecom industry," Malaysian J. Comput. Sci., vol. 28, no. 3, pp. 213–220, 2015, doi: 10.22452/mjcs.vol28no3.3.
[2] X. Han et al., "An Efficient Genetic Algorithm for Optimization Problems with Time-Consuming Fitness Evaluation," Int. J. Comput. Methods, vol. 12, no. 01, p. 1350106, Feb. 2015, doi: 10.1142/S0219876213501065.
[3] W. F. Mahmudy, R. M. Marian, and L. H. S. Luong, "Real Coded Genetic Algorithms for Solving Flexible Job-Shop Scheduling Problem - Part I: Modelling," Adv. Mater. Res., vol. 701, pp. 359–363, May 2013, doi: 10.4028/www.scientific.net/AMR.701.359.
[4] N. C. Long and P. Meesad, "Meta-heuristic algorithms applied to the optimization of type-1 and type 2 TSK fuzzy logic systems for sea water level prediction," in 2013 IEEE 6th International Workshop on Computational Intelligence and Applications (IWCIA), 2013, pp. 69–74, doi: 10.1109/IWCIA.2013.6624787.
[5] S. Elhag, A. Fernández, S. Alshomrani, and F. Herrera, "Evolutionary Fuzzy Systems: A Case Study for Intrusion Detection Systems," 2019, pp. 169–190, doi: 10.1007/978-3-319-91341-4_9.
[6] D. Mester and O. Bräysy, "Active guided evolution strategies for large-scale vehicle routing problems with time windows," Comput. Oper. Res., vol. 32, no. 6, pp. 1593–1614, Jun. 2005, doi: 10.1016/j.cor.2003.11.017.
[7] S. Siu, S.-S. Yang, C.-M. Lee, and C.-L. Ho, "Improving the Back-Propagation Algorithm Using Evolutionary Strategy," IEEE Trans. Circuits Syst. II Express Briefs, vol. 54, no. 2, pp. 171–175, Feb. 2007, doi: 10.1109/TCSII.2006.883226.
[8] F. Jiménez, G. Sánchez, J. M. García, G. Sciavicco, and L. Miralles, “Multi-objective evolutionary feature selection for online sales forecasting,” Neurocomputing, vol. 234, pp. 75–92, 2017, doi: 10.1016/j.neucom.2016.12.045.
[9] S. Iqbal, C. Zhang, M. Arif, M. Hassan, and S. Ahmad, "A new fuzzy time series forecasting method based on clustering and weighted average approach," J. Intell. Fuzzy Syst., no. Preprint, pp. 1–10, 2020, doi: 10.3233/JIFS-179693.
[10] X. Xie, L. Lin, and S. Zhong, "Process Takagi–Sugeno model: A novel approach for handling continuous input and output functions and its application to time series prediction," Knowledge-Based Syst., vol. 63, pp. 46–58, Jun. 2014, doi: 10.1016/j.knosys.2014.03.012.
[11] J. L. Chavez-Hurtado and J. H. Cortes-Fregoso, "Forecasting Mexican inflation using neural networks," in 23rd International Conference on Electronics, Communications and Computing, CONIELECOMP 2013, 2013, doi: 10.1109/CONIELECOMP.2013.6525753.
[12] B. Ruprecht et al., "Possibilistic Clustering Enabled Neuro Fuzzy Logic," in 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2020, pp. 1–8, doi: 10.1109/FUZZ48607.2020.9177593.
[13] P. Nikdel, M. Hosseinpour, M. A. Badamchizadeh, and M. A. Akbari, "Improved Takagi–Sugeno fuzzy model-based control of flexible joint robot via Hybrid-Taguchi genetic algorithm," Eng. Appl. Artif. Intell., vol. 33, pp. 12–20, Aug. 2014, doi: 10.1016/j.engappai.2014.03.009.
[14] W.-H. Ho, S.-H. Chen, and J.-H. Chou, "Optimal control of Takagi–Sugeno fuzzy-model-based systems representing dynamic ship positioning systems," Appl. Soft Comput., vol. 13, no. 7, pp. 3197–3210, Jul. 2013, doi: 10.1016/j.asoc.2013.02.019.
[15] B. Gharleghi, A. Hassan Shaari, and N. Shafighi, "Predicting exchange rates using a novel 'cointegration based neuro-fuzzy system,'” Int. Econ., vol. 137, pp. 88–103, May 2014, doi: 10.1016/j.inteco.2013.12.001.
[16] Y. W. Kerk, K. M. Tay, and C. P. Lim, “Monotone Fuzzy Rule Interpolation for Practical Modelling of the Zero-Order TSK Fuzzy Inference System,” IEEE Trans. Fuzzy Syst., 2021, pp. 1-1, doi: 10.1109/TFUZZ.2021.3057239.
[17] Y. Cui, D. Wu, and J. Huang, “Optimize TSK Fuzzy Systems for Classification Problems: Minibatch Gradient Descent With Uniform Regularization and Batch Normalization,” IEEE Trans. Fuzzy Syst., vol. 28, no. 12, pp. 3065–3075, 2020, doi: 10.1109/TFUZZ.2020.2967282
[18] A. G. Sarip, M. B. Hafez, and M. N. Daud, “Application Of Fuzzy Regression Model For Real Estate Price Prediction,” Malaysian J. Comput. Sci., vol. 29, no. 1, pp. 15–27, Mar. 2016, doi: 10.22452/mjcs.vol29no1.2.
[19] G. L. Du, Z. Wang, C. Li, and P. X. Liu, “A TSK-type Convolutional Recurrent Fuzzy Network for Predicting Driving Fatigue,” IEEE Trans. Fuzzy Syst., vol. 15, no. 5, pp. 1–1, 2020, doi: 10.1109/TFUZZ.2020.2992856.
[20] R. M. Pattanayak, H. S. Behera, and S. Panigrahi, “A novel probabilistic intuitionistic fuzzy set based model for high order fuzzy time series forecasting,” Eng. Appl. Artif. Intell., vol. 99, p. 104136, Mar. 2021, doi: 10.1016/j.engappai.2020.104136.
[21] P.-C. Chang, J.-L. Wu, and J.-J. Lin, “A Takagi–Sugeno fuzzy model combined with a support vector regression for stock trading forecasting,” Appl. Soft Comput., vol. 38, pp. 831–842, Jan. 2016, doi: 10.1016/j.asoc.2015.10.030.
[22] A. R. Lima, A. J. Cannon, and W. W. Hsieh, “Nonlinear regression in environmental sciences by support vector machines combined with evolutionary strategy,” Comput. Geosci., vol. 50, pp. 136–144, Jan. 2013, doi: 10.1016/j.cageo.2012.06.023.
[23] N. R. Sari, W. F. Mahmudy, and A. P. Wibawa, “The effectiveness of hybrid backpropagation Neural Network model and TSK Fuzzy Inference System for inflation forecasting,” J. Telecommun. Electron. Comput. Eng., 2017. Available at: Google Scholar.
[24] “Data Inflasi,” Bank Indonesia. [Online]. Available: https://www.bi.go.id/id/statistik/indikator/data-inflasi.aspx. [Accessed: 22-Oct-2015].
[25] E. J. Douglas, D. A. Shepherd, and C. Prentice, “Using fuzzy-set qualitative comparative analysis for a finer-grained understanding of entrepreneurship,” J. Bus. Ventur., vol. 35, no. 1, p. 105970, 2020, doi: 10.1016/j.jbusvent.2019.105970.
[26] F. A. Mohd-Rahim, C. Wang, H. Boussabaine, and H. Abdul-Rahman, “Risk prevention and deduction in software development using fuzzy membership function,” Malaysian J. Comput. Sci., 2015, doi: 10.22452/mjcs.vol28no3.4.
[27] P. Prokopowicz and D. Ślȩzak, “Ordered Fuzzy Numbers: Sources and Intuitions,” 2017, pp. 47–56, doi: 10.1007/978-3-319-59614-3_3.
[28] R. Logambigai, S. Ganapathy, and A. Kannan, “Energy–efficient grid–based routing algorithm using intelligent fuzzy rules for wireless sensor networks,” Comput. Electr. Eng., vol. 68, pp. 62–75, May 2018, doi: 10.1016/j.compeleceng.2018.03.036.
[29] D. Enke and N. Mehdiyev, “A Hybrid Neuro-fuzzy Model to Forecast Inflation,” Procedia Comput. Sci., vol. 36, pp. 254–260, 2014, doi: 10.1016/j.procs.2014.09.088.
[30] P. Pirayesh, H. Motameni, and E. Akbari, “Comparison of two Defuzzification methods of Mean of Max and Central Average in Morphology of composition functions in Persian Sentences,” J. Adv. Comput. Res., vol. 11, no. 2, pp. 17–29, 2020, available at : http://jacr.iausari.ac.ir/article_677113.html.
[31] M. G. Arjanaki and M. Pourgholi, “Takagi-Sugeno (TS) Fuzzy model-basedobserver design for glucose-insulin system in diabetes type 1: An LMI approach,” in 2020 28th Iranian Conference on Electrical Engineering (ICEE), 2020, pp. 1–5, doi: 10.1109/ICEE50131.2020.9260768.
[32] E. Salazar, “Integrating Evolution Strategies into Genetic Algorithms with Fuzzy Inference Evaluation to Solve a Steelmaking and Continuous Casting Scheduling Problem,” In: Sim K., Kaufmann P. (eds) Applications of Evolutionary Computation, EvoApplications 2018, Lecture Notes in Computer Science, Springer, 2018, vol 10784, pp. 561–577, doi: 10.1007/978-3-319-77538-8_39.
[33] W. F. Mahmudy, R. M. Mariana, and L. H. S. Luong, “Hybrid genetic algorithms for multi-period part type selection and machine loading problems in flexible manufacturing system,” in 2013 IEEE International Conference on Computational Intelligence and Cybernetics (CYBERNETICSCOM), 2013, pp. 126–130, doi: 10.1109/CyberneticsCom.2013.6865795.
[34] Y. Wang, “A Genetic Algorithm with the Mixed Heuristics for Traveling Salesman Problem,” Int. J. Comput. Intell. Appl., vol. 14, no. 01, p. 1550003, Mar. 2015, doi: 10.1142/S1469026815500030.
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