An innovative model to mitigate the impact of oil and steel price dynamics on the oil & gas sector projects
Resumo
This paper addresses the development and application of an innovative model to analyze the historical price series of commodities, significantly impacting the profitability of Brazil’s oil and gas projects. The experiment focuses on six historical price series of commodities critical to significant oil and gas exploration companies. It highlights the volatility of steel prices in the Brazilian and international markets and their direct impact on the key suppliers and explorers in the sector. The research introduces an advanced model, employing Deep Learning techniques with automated hyperparameters to optimize the selection of the most effective model for each dataset. This selection is based on a score of seven distinct metrics, ensuring the choice of the most suitable model to predict market trends relevant to the Oil and Gas sector.
Palavras-chave:
Oil and Gas, Commodity, Financial Risk Analysis, Deep Learning, Time Serie Forecast
Referências
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Sen, A. and Dutta Choudhury, K. (2024). Forecasting the crude oil prices for last four decades using deep learning approach. Resources Policy, 88:104438.
Thomas, J. B. and K.V., S. (2023). Neural architecture search algorithm to optimize deep transformer model for fault detection in electrical power distribution systems. Engineering Applications of Artificial Intelligence, 120:105890.
Wang, J., Zhao, W., Tsai, F.-S., Jin, H., Tan, J., and Su, C. (2023). A study of crude oil futures price volatility based on multi-dimensional data from event-driven and deep learning perspectives. Applied Soft Computing, 146:110548.
Xu, Y., Liu, T., and Du, P. (2024). Volatility forecasting of crude oil futures based on bi-lstm-attention model: The dynamic role of the covid-19 pandemic and the russian-ukrainian conflict. Resources Policy, 88:104319.
Yang, K., Cheng, Z., Li, M., Wang, S., and Wei, Y. (2024). Fortify the investment performance of crude oil market by integrating sentiment analysis and an interval-based trading strategy. Applied Energy, 353:122102.
Athanasopoulos, G., Hyndman, R. J., Kourentzes, N., and Panagiotelis, A. (2024). Forecast reconciliation: A review. International Journal of Forecasting, 40(2):430–456.
Bai, S., Kolter, J. Z., and Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. CoRR, abs/1803.01271.
Challu, C., Olivares, K. G., Oreshkin, B. N., Garza, F., Mergenthaler-Canseco, M., and Dubrawski, A. (2022). N-hits: Neural hierarchical interpolation for time series forecasting.
Chen, Y., Kang, Y., Chen, Y., and Wang, Z. (2020). Probabilistic forecasting with temporal convolutional neural network.
Cihan, P. (2024). Comparative performance analysis of deep learning, classical, and hybrid time series models in ecological footprint forecasting. Applied Sciences, 14(4):1479.
Das, A., Kong, W., Leach, A., Mathur, S., Sen, R., and Yu, R. (2024). Long-term forecasting with tide: Time-series dense encoder.
Fang, Y., Wang, W., Wu, P., and Zhao, Y. (2023). A sentiment-enhanced hybrid model for crude oil price forecasting. Expert Systems with Applications, 215:119329.
Flunkert, V., Salinas, D., and Gasthaus, J. (2017). Deepar: Probabilistic forecasting with autoregressive recurrent networks. CoRR, abs/1704.04110.
Foroutan, P. and Lahmiri, S. (2024). Deep learning-based spatial-temporal graph neural networks for price movement classification in crude oil and precious metal markets. Machine Learning with Applications, 16:100552.
Guo, L., Huang, X., Li, Y., and Li, H. (2023). Forecasting crude oil futures price using machine learning methods: Evidence from china. Energy Economics, 127:107089.
He, K., Zheng, L., Yang, Q., Wu, C., Yu, Y., and Zou, Y. (2023). Crude oil price prediction using temporal fusion transformer model. Procedia Computer Science, 221:927–932. Tenth International Conference on Information Technology and Quantitative Management (ITQM 2023).
Herzen, J., Lässig, F., Piazzetta, S. G., Neuer, T., Tafti, L., Raille, G., Pottelbergh, T. V., Pasieka, M., Skrodzki, A., Huguenin, N., Dumonal, M., Kościsz, J., Bader, D., Gusset, F., Benheddi, M., Williamson, C., Kosinski, M., Petrik, M., and Grosch, G. (2022). Darts: User-friendly modern machine learning for time series.
Jorion, P. (2007). Value at risk: the new benchmark for managing financial risk. McGraw-Hill.
Kazmi, H., Fu, C., and Miller, C. (2023). Ten questions concerning data-driven modelling and forecasting of operational energy demand at building and urban scale. Building and Environment, 239:110407.
Knuth, D. E. (1984). The TEX Book. Addison-Wesley, 15th edition.
Maiti, R., Menon, B. G., and Abraham, A. (2024). Ensemble empirical mode decomposition based deep learning models for forecasting river flow time series. Expert Systems with Applications, 255:124550.
Mohsin, M. and Jamaani, F. (2023a). A novel deep-learning technique for forecasting oil price volatility using historical prices of five precious metals in context of green financing–a comparison of deep learning, machine learning, and statistical models. Resources Policy, 86:104216.
Mohsin, M. and Jamaani, F. (2023b). A novel deep-learning technique for forecasting oil price volatility using historical prices of five precious metals in context of green financing – a comparison of deep learning, machine learning, and statistical models. Resources Policy, 86:104216.
Oreshkin, B. N., Carpov, D., Chapados, N., and Bengio, Y. (2019). N-BEATS: neural basis expansion analysis for interpretable time series forecasting. CoRR, abs/1905.10437.
Oreshkin, B. N., Dudek, G., and Pelka, P. (2020). N-BEATS neural network for mid-term electricity load forecasting. CoRR, abs/2009.11961.
Orji, U. and Ukwandu, E. (2024). Machine learning for an explainable cost prediction of medical insurance. Machine Learning with Applications, 15:100516.
Panja, M., Chakraborty, T., Nadim, S. S., Ghosh, I., Kumar, U., and Liu, N. (2023). An ensemble neural network approach to forecast dengue outbreak based on climatic condition. Chaos, Solitons Fractals, 167:113124.
Salehin, I., Islam, M. S., Saha, P., Noman, S., Tuni, A., Hasan, M. M., and Baten, M. A. (2024). Automl: A systematic review on automated machine learning with neural architecture search. Journal of Information and Intelligence, 2(1):52–81.
Sen, A. and Dutta Choudhury, K. (2024). Forecasting the crude oil prices for last four decades using deep learning approach. Resources Policy, 88:104438.
Thomas, J. B. and K.V., S. (2023). Neural architecture search algorithm to optimize deep transformer model for fault detection in electrical power distribution systems. Engineering Applications of Artificial Intelligence, 120:105890.
Wang, J., Zhao, W., Tsai, F.-S., Jin, H., Tan, J., and Su, C. (2023). A study of crude oil futures price volatility based on multi-dimensional data from event-driven and deep learning perspectives. Applied Soft Computing, 146:110548.
Xu, Y., Liu, T., and Du, P. (2024). Volatility forecasting of crude oil futures based on bi-lstm-attention model: The dynamic role of the covid-19 pandemic and the russian-ukrainian conflict. Resources Policy, 88:104319.
Yang, K., Cheng, Z., Li, M., Wang, S., and Wei, Y. (2024). Fortify the investment performance of crude oil market by integrating sentiment analysis and an interval-based trading strategy. Applied Energy, 353:122102.
Publicado
17/11/2024
Como Citar
FLOR, Aguinaldo Júnio; FRANÇA, Luis.
An innovative model to mitigate the impact of oil and steel price dynamics on the oil & gas sector projects. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 21. , 2024, Belém/PA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 448-459.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2024.244134.
