Application of Metasynthesis in Identifying New Combined Genetic Algorithm Methods to Solve Problems in Oil Price Forecasting

Heidari, Atefeh; Daghighiasli, Alireza; Abbassi, Ebrahim; Damankeshideh, Marjan

doi:10.22034/kps.2024.437731.1174

Application of Metasynthesis in Identifying New Combined Genetic Algorithm Methods to Solve Problems in Oil Price Forecasting

Document Type : Original Research Manuscripts

Authors

Atefeh Heidari ¹

Alireza Daghighiasli ²

Ebrahim Abbassi ¹

Marjan Damankeshideh ¹

¹ Department of Economics, Central Tehran Branch, Islamic Azad university, Tehran, Iran

² Department of Economics, Central Tehran Branch, Islamic Azad university, Tehran,

https://doi.org/10.22034/kps.2024.437731.1174

Abstract

The current research seeks to identify new combined genetic algorithm methods to solve price forecasting It was oil.
Effective factors were identified by using a systematic and meta-composite review approach, and performing the 7 steps of the Sandelowski and Barroso method. Among 4340 articles, 54 articles were selected based on the CASP method. In this context, in order to measure reliability and quality control, the Kappa index was used, and its value was identified for the identified indicators at the level of excellent agreement.
The results of the data analysis collected in the ATLAS TI software led to the identification of 7 categories and 26 primary codes of new combined genetic algorithm methods to solve completely difficult problems. Based on the done coding, 7 categories and 26 primary codes were identified. The identified categories are: component design, supply network, planning, forecasting, inventory control, information security, segregation and evaluation.
The combination of genetic algorithm with different methods due to its ability to distinguish features and optimize parameters can lead to significant improvements in the field of oil price prediction. The use of genetic algorithm in solving oil price forecasting problems as an evolutionary and artificial intelligence approach makes it possible to integrate diverse and complex information in forecasting models. By designing the appropriate components as genes that represent important economic, geographic and political features, the most optimal genetic combinations can be created to increase the accuracy and performance of prediction models.

Keywords

Artificial neural network

Text Analysis

Functional knowledge

Subjects

Knowledge Extraction

Abdollahi, H. (2020). A novel hybrid model for forecasting crude oil price based on time series decomposition. Applied energy, 267, 115035.10.1016/j.apenergy.2020.115035

Asghari, H., Omid, M., Malekinejad, Z., & Omid, A. (2018). Forecasting global crude oil demand by using vector autoregressive, autoregressive distributed lag, and gravitational search algorithms. Future Studies in Management, 29(4), 101-118.

Behmiri, N. B., & Manso, J. R. P. (2013). How crude oil consumption impacts on economic growth of Sub-Saharan Africa?. Energy, 54, 74-83.

https://doi.org/10.1016/j.energy.2013.02.052

Bristone, M., Prasad, R., & Abubakar, A. A. (2020). CPPCNDL: Crude oil price prediction using complex network and deep learning algorithms. Petroleum, 6(4), 353-361.

https://doi.org/10.1016/j.petlm.2019.11.009.

Bu, H. (2014). Effect of inventory announcements on crude oil price volatility. Energy Economics, 46, 485-494.

https://doi.org/10.1016/j.eneco.2014.05.015

Cao, J., Li, Z., & Li, J. (2019). Financial time series forecasting model based on CEEMDAN and LSTM. Physica A: Statistical mechanics and its applications, 519, 127-139.

https://doi.org/10.1016/j.physa.2018.11.061

Chen, Y., He, K., & Tso, G. K. (2017). Forecasting crude oil prices: a deep learning based model. Procedia computer science, 122, 300-307.

https://doi.org/10.1016/j.procs.2017.11.373

Chen, J. C., & Wang, X. A. (2017, December). Development of fuzzy logic and genetic fuzzy commodity price prediction systems—An industrial case study. In 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 432-435). IEEE.

Chen, Z., Goh, H. S., Sin, K. L., Lim, K., Chung, N. K. H., & Liew, X. Y. (2021). Automated agriculture commodity price prediction system with machine learning techniques. arXiv preprint arXiv:2106.12747.

Claussen, K. L., & Uhrig, D. J. W. (1994). Cash soybean price prediction with neural networks. In Proceedings of the NCR-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management. Chicago, IL.[http://www. farmdoc. uiuc. edu/nccc134].

ELYAKOV, A. L., ELYAKOVA, I. D., Andreyevich, T., TOTONOVA, E., & FEDOROV, A. A. (2018). World oil prices: a retrospective analysis and elevation of factors. Corpus ID, 54783239.

Firouzi Jahantigh, F., & Dehghani, S. (2015). Application of genetic algorithm in optimization of neural network architecture and oil price prediction (GADDN). Applied Economic Studies of Iran (Applied Economic Studies), 5(20), 122-97.https://dorl.net/dor/20.1001.1.22519165.1397.9.35.8.1

Gao, S., & Lei, Y. (2017). A new approach for crude oil price prediction based on stream learning. Geoscience Frontiers, 8(1), 183-187. doi: 10.1016/j.gsf.2016.08.002

Gao, S. & Lei, Y. (2017). A new approach for crude oil price prediction based on stream Learning, Geoscience Frontiers 8, 183-187. https://doi.org/10.1016/j.gsf.2016.08.002

Gao, X., Fang, W., An, F., & Wang, Y. (2017). Detecting method for crude oil price fluctuation mechanism under different periodic time series. Applied energy, 192, 201-212.

https://doi.org/10.1016/j.apenergy.2017.02.014

Gargano, A., & Timmermann, A. (2014). Forecasting commodity price indexes using macroeconomic and financial predictors. International Journal of Forecasting, 30(3), 825-843.

https://doi.org/10.1016/j.ijforecast.2013.09.003

Gunawan, R., & Khodra, M. L. (2013, November). Commodity price prediction using neural network case study: Crude palm oil price. In 2013 International Conference on Computer, Control, Informatics and Its Applications (IC3INA) (pp. 243-248). IEEE.

Gupta, V., & Pandey, A. (2018). Crude oil price prediction using LSTM networks. International Journal of Computer and Information Engineering, 12(3), 226-230.

Aydogmus, H. Y., Ekinci, A., & Halil, I. (2015). Optimizing the monthly crude oil price forecasting accuracy via bagging ensemble models. Journal of Economics and International Finance, 7(5), 127.

https://doi.org/10.5897/JEIF2014.0629

Hasan, M., Abedin, M. Z., Hajek, P., Coussement, K., Sultan, M. N., & Lucey, B. (2024). A blending ensemble learning model for crude oil price forecasting. Annals of Operations Research, 1-31.

http://dx.doi.org/10.2139/ssrn.4153206

He, H., Sun, M., Li, X., & Mensah, I. A. (2022). A novel crude oil price trend prediction method: Machine learning classification algorithm based on multi-modal data features. Energy, 244, 122706.

https://doi.org/10.1016/j.energy.2021.122706

Huang, L., & Wang, J. (2018). Global crude oil price prediction and synchronization based accuracy evaluation using random wavelet neural network. Energy, 151, 875-888.

https://doi.org/10.1016/j.energy.2018.03.099

Huang, L., & Wang, J. (2018). Global crude oil price prediction and synchronization based accuracy evaluation using random wavelet neural network. Energy, 151, 875-888.

https://doi.org/10.1016/j.energy.2018.03.099

Jahanshahi, H., Uzun, S., Kaçar, S., Yao, Q., & Alassafi, M. O. (2022). Artificial intelligence-based prediction of crude oil prices using multiple features under the effect of Russia–Ukraine war and COVID-19 pandemic. Mathematics, 10(22), 4361.

https://doi.org/10.3390/math10224361

Jovanovic, L., Jovanovic, D., Bacanin, N., Jovancai Stakic, A., Antonijevic, M., Magd, H., ... & Zivkovic, M. (2022). Multi-step crude oil price prediction based on lstm approach tuned by salp swarm algorithm with disputation operator. Sustainability, 14(21), 14616.

https://doi.org/10.3390/su142114616

Karasu, S., & Altan, A. (2022). Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization. Energy, 242, 122964.

https://doi.org/10.1016/j.energy.2021.122964

Karathanasopoulos, A., Zaremba, A., Osman, M., & Mikutowski, M. (2019). Oil forecasting using artificial intelligence. Theoretical Economics Letters, 9(7), 2283-2290.

https://doi.org/10.4236/tel.2019.97144

Kohzadi, N., Boyd, M. S., Kermanshahi, B., & Kaastra, I. (1996). A comparison of artificial neural network and time series models for forecasting commodity prices. Neurocomputing, 10(2), 169-181.

https://doi.org/10.1016/0925-2312(95)00020-8

Kpodar, K., & Imam, P. A. (2021). To pass (or not to pass) through international fuel price changes to domestic fuel prices in developing countries: What are the drivers?. Energy Policy, 149, 111999.

https://doi.org/10.1016/j.enpol.2020.111999

Kulkarni, S., & Haidar, I. (2009). Forecasting model for crude oil price using artificial neural networks and commodity futures prices. arXiv preprint arXiv:0906.4838.

Li, R., Hu, Y., Heng, J., & Chen, X. (2021). A novel multiscale forecasting model for crude oil price time series. Technological Forecasting and Social Change, 173, 121181.

https://doi.org/10.1016/j.techfore.2021.121181

Liu, Z., Ding, Z., Lv, T., Wu, J. S., & Qiang, W. (2019). Financial factors affecting oil price change and oil-stock interactions: a review and future perspectives. Natural Hazards, 95, 207-225.

https://doi.org/10.1007/s11069-018-3473-y.

Lu, Q., Sun, S., Duan, H., & Wang, S. (2021). Analysis and forecasting of crude oil price based on the variable selection-LSTM integrated model. Energy Informatics, 4(Suppl 2), 47.

https://doi.org/10.1186/s42162-021-00166-4

Manickavasagam, J., Visalakshmi, S., & Apergis, N. (2020). A novel hybrid approach to forecast crude oil futures using intraday data. Technological Forecasting and Social Change, 158, 120126.

https://doi.org/10.1016/j.techfore.2020.120126

Mohammadi Almoti, M., Hadadi, M., & Nadimi, Y. (2020). Investigating the Weak Efficiency Hypothesis in Two Low-Volatility and High-Volatility Regimes of the OPEC Crude Oil Market. Investment Knowledge, 9(33), 109-127.

Nademi, A., & Nademi, Y. (2018). Forecasting crude oil prices by a semiparametric Markov switching model: OPEC, WTI, and Brent cases. Energy economics, 74, 757-766.

https://doi.org/10.1016/j.eneco.2018.06.020

Ouyang, H., Wei, X., & Wu, Q. (2019). Agricultural commodity futures prices prediction via long-and short-term time series network. Journal of Applied Economics, 22(1), 468-483.

https://doi.org/10.1080/15140326.2019.1668664

Parida, N., Mishra, D., Das, K., & Rout, N. K. (2021). Development and performance evaluation of hybrid KELM models for forecasting of agro-commodity price. Evolutionary Intelligence, 14, 529-544.

https://doi.org/10.1007/s12065-019-00295-6

Peng, J., Li, Z., & Drakeford, B. M. (2020). Dynamic characteristics of crude oil price fluctuation—from the perspective of crude oil price influence mechanism. Energies, 13(17), 4465.

https://doi.org/10.3390/en13174465

Razavi, A., Salimifar, M., Mostafavi, M., & Baki Heskooyi, M. (2014). Short-term impact of financial markets on the behavior of heavy Iranian crude oil prices. Quantitative Economics Quarterly, 11(2), 115-143. http://dx.doi.org/10.5539/mas.v10n3p45

Rostami, M., Naqvipour, M., Holy Bayat, M. (2017). Oil market volatility model based on regime choice approach. Financial Engineering and Securities Management, 9(35), 179-196.https://dorl.net/dor/20.1001.1.22519165.1397.9.35.8.1D

Sehgal, N., & Pandey, K. K. (2015). Artificial intelligence methods for oil price forecasting: a review and evaluation. Energy Systems, 6, 479-506.

https://doi.org/10.1007/s12667-015-0151-y

Sen, A., Dutta Choudhury, K., & Kumar Datta, T. (2023). An analysis of crude oil prices in the last decade (2011-2020): With deep learning approach. Plos one, 18(3), e0268996.

https://doi.org/10.1371/journal.pone.0268996

Shabri, A., & Samsudin, R. (2014). Daily crude oil price forecasting using hybridizing wavelet and artificial neural network model. Mathematical Problems in Engineering, 2014(1), 201402.

https://doi.org/10.1155/2014/201402

Shahbazi, K., & Salimian, S. (2015). Predicting oil prices using meta-analysis method. Journal of Energy Economics Studies, 11, 67-93. http://iiesj.ir/article-1-301-en.html

Sun, S., Sun, Y., Wang, S., & Wei, Y. (2018). Interval decomposition ensemble approach for crude oil price forecasting. Energy Economics, 76, 274-287.

https://doi.org/10.1016/j.eneco.2018.10.015

Takroosta A, Mohajeri P, Mohammadi T, Shakeri A, & Ghasemi A. (2019). An Analysis of Oil Prices Considering the Political Risk of OPEC. Jemr, 10 (37), 105-138. http://jemr.khu.ac.ir/article-1-1832-en.html

Xiong, T., Li, C., & Bao, Y. (2018). Seasonal forecasting of agricultural commodity price using a hybrid STL and ELM method: Evidence from the vegetable market in China. Neurocomputing, 275, 2831-2844.

https://doi.org/10.1016/j.neucom.2017.11.053

Yu, L., Dai, W., & Tang, L. (2016). A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting. Engineering Applications of Artificial Intelligence, 47, 110-121.

https://doi.org/10.1016/j.engappai.2015.04.016

Yu, L., Zhang, X., & Wang, S. (2017). Assessing potentiality of support vector machine method in crude oil price forecasting. EURASIA Journal of Mathematics, Science and Technology Education, 13(12), 7893-7904.

https://doi.org/10.12973/ejmste/77926

Yu, L., Zhao, Y., & Tang, L. (2014). A compressed sensing based AI learning paradigm for crude oil price forecasting. Energy Economics, 46, 236-245.

https://doi.org/10.1016/j.eneco.2014.09.019

Zhang, Y., & Na, S. (2018). A novel agricultural commodity price forecasting model based on fuzzy information granulation and MEA‐SVM model. Mathematical Problems in Engineering, 2018(1), 2540681.

https://doi.org/10.1155/2018/3719564

Zhang, J., & He, Q. Z. (2021). Dynamic Cross‐Market Volatility Spillover Based on MSV Model: Evidence from Bitcoin, Gold, Crude Oil, and Stock Markets. Complexity, 2021(1), 9912418.

Zhao, L. T., Wang, Z. J., Wang, S. P., & He, L. Y. (2021). Predicting oil prices: an analysis of oil price volatility cycle and financial markets. Emerging Markets Finance and Trade, 57(4), 1068-1087.

https://doi.org/10.1080/1540496X.2019.1706045