* Corresponding Author
Hide
Show all
Flower Pollination Inspired Algorithm on Exchange Rates Prediction Case
https://doi.org/10.22146/ijccs.84223 I Nyoman Prayana Trisna(1*), Afiahayati Afiahayati(2), Muhammad Auzan(3)
(1) Information Technology Study Program, Udayana University, Bali
(2) Dept. of Computer Science and Electronics, Universitas Gadjah Mada, Yogyakarta
(3) Dept. of Computer Science and Electronics, Universitas Gadjah Mada, Yogyakarta
(*) Corresponding Author
Abstract
The flower pollination algorithm is a bio-inspired system that adapts a similar process to a genetic algorithm that aims for optimization problems. In this research, we examine the utilization of the flower pollination algorithm in linear regression for currency exchange cases. Each solution represents the regression coefficients. The population size for the solutions and the switching probability between global pollination and local pollination is experimented with in this research. The result shows that the final solution is obtained using a larger population and higher switch probability. Furthermore, our research finds that the increasing population size leads to considerable running time, where the probability of global pollination just slightly increases the running time
Keywords
Full Text:
PDFReferences
T. Henttu-Aho, ‘The role of rolling forecasting in budgetary control systems: Reactive and proactive types of planning’, Journal of management control, vol. 29, no. 3–4, pp. 327–360, 2018. C. Foroni, M. Marcellino, and D. Stevanovic, ‘Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis’, International Journal of Forecasting, vol. 38, no. 2, pp. 596–612, 2022. M. S. Islam and E. Hossain, ‘Foreign exchange currency rate prediction using a GRU-LSTM hybrid network’, Soft Computing Letters, vol. 3, p. 100009, 2021. M. Cai, M. Pipattanasomporn, and S. Rahman, ‘Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques’, Applied energy, vol. 236, pp. 1078–1088, 2019. B. Njindan Iyke, ‘The disease outbreak channel of exchange rate return predictability: Evidence from COVID-19’, Emerging Markets Finance and Trade, vol. 56, no. 10, pp. 2277–2297, 2020. S. K. Tamang, P. D. Singh, and B. Datta, ‘Forecasting of Covid-19 cases based on prediction using artificial neural network curve fitting technique’, Global Journal of Environmental Science and Management, vol. 6, no. Special(Covid-19), pp. 53–64, 2020. W. Cao, W. Zhu, W. Wang, Y. Demazeau, and C. Zhang, ‘A deep coupled LSTM approach for USD/CNY exchange rate forecasting’, IEEE Intelligent Systems, vol. 35, no. 2, pp. 43–53, 2020. P. L. Seabe, C. R. B. Moutsinga, and E. Pindza, ‘Forecasting cryptocurrency prices using LSTM, GRU, and bi-directional LSTM: A deep learning approach’, Fractal and Fractional, vol. 7, no. 2, p. 203, 2023. H. Lu, M. Azimi, and T. Iseley, ‘Short-term load forecasting of urban gas using a hybrid model based on improved fruit fly optimization algorithm and support vector machine’, Energy Reports, vol. 5, pp. 666–677, 2019. N. Nabipour, M. Dehghani, A. Mosavi, and S. Shamshirband, ‘Short-term hydrological drought forecasting based on different nature-inspired optimization algorithms hybridized with artificial neural networks’, IEEE Access, vol. 8, pp. 15210–15222, 2020. S. Wang, Z. Tang, and B. Chai, ‘Exchange rate prediction model analysis based on improved artificial neural network algorithm’, in 2016 International Conference on Communication and Electronics Systems (ICCES), 2016, pp. 1–5. S. A. Shahriar et al., ‘Potential of Arima-ann, Arima-SVM, dt and catboost for atmospheric PM2. 5 forecasting in bangladesh’, Atmosphere, vol. 12, no. 1, p. 100, 2021. M. Durairaj and B. H. K. Mohan, ‘A review of two decades of deep learning hybrids for financial time series prediction’, International Journal on Emerging Technologies, vol. 10, no. 3, pp. 324–331, 2019. A. Sespajayadi, I. Nurtanio, and Others, ‘Technical data analysis for movement prediction of Euro to USD using genetic algorithm-neural network’, in 2015 International Seminar on Intelligent Technology and Its Applications (ISITIA), 2015, pp. 23–26. D. K. Sharma, H. S. Hota, K. Brown, and R. Handa, ‘Integration of genetic algorithm with artificial neural network for stock market forecasting’, International Journal of System Assurance Engineering and Management, vol. 13, no. Suppl 2, pp. 828–841, 2022. X.-S. Yang, ‘Flower pollination algorithm for global optimization’, in Unconventional Computation and Natural Computation: 11th International Conference, UCNC 2012, Orléan, France, September 3-7, 2012. Proceedings 11, 2012, pp. 240–249. A. Qonita, A. G. Pertiwi, and T. Widiyaningtyas, ‘Prediction of rupiah against us dollar by using arima’, in 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 2017, pp. 1–5. F. Raheem and N. Iqbal, ‘Forecasting foreign exchange rate: Use of FbProphet’, in 2021 International Research Conference on Smart Computing and Systems Engineering (SCSE), 2021, vol. 4, pp. 44–48. R. S. Al-Gounmeein and M. T. Ismail, ‘Forecasting the exchange rate of the Jordanian Dinar versus the US dollar using a Box-Jenkins seasonal ARIMA model’, International Journal of Mathematics and Computer Science, vol. 15, no. 1, pp. 27–40, 2020. E. Dave, A. Leonardo, M. Jeanice, and N. Hanafiah, ‘Forecasting Indonesia exports using a hybrid model ARIMA-LSTM’, Procedia Computer Science, vol. 179, pp. 480–487, 2021. M. Zivkovic, N. Bacanin, A. Djordjevic, M. Antonijevic, I. Strumberger, and T. A. Rashid, ‘Hybrid genetic algorithm and machine learning method for covid-19 cases prediction’, in Proceedings of international conference on sustainable expert systems: ICSES 2020, 2021, pp. 169–184. Z. A. A. Alyasseri, A. T. Khader, M. A. Al-Betar, M. A. Awadallah, and X.-S. Yang, ‘Variants of the flower pollination algorithm: a review’, Nature-inspired algorithms and applied optimization, pp. 91–118, 2018. A. Hassanat, K. Almohammadi, E. Alkafaween, E. Abunawas, A. Hammouri, and V. B. S. Prasath, ‘Choosing mutation and crossover ratios for genetic algorithms—a review with a new dynamic approach’, Information, vol. 10, no. 12, p. 390, 2019. C. Tsallis, S. V. F. Levy, A. M. C. Souza, and R. Maynard, ‘Statistical-mechanical foundation of the ubiquity of Lévy distributions in nature’, Physical Review Letters, vol. 75, no. 20, p. 3589, 1995. M. Abdel-Baset and I. M Hezam, ‘An effective hybrid flower pollination and genetic algorithm for constrained optimization problems’, Advanced Engineering Technology and Application, vol. 4, no. 3, pp. 28–35, 2015.
DOI: https://doi.org/10.22146/ijccs.84223
Article Metrics
Abstract views : 1378
| 
views : 814
Refbacks
- There are currently no refbacks.
Copyright (c) 2023 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats1