An innovative model to mitigate the impact of oil and steel price dynamics on the oil & gas sector projects - Flor, Aguinaldo Júnio; França, Luis (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2024: Anais do XXI Encontro Nacional de Inteligência Artificial e ComputacionalDO - 10.5753/eniac.2024.244134, 2024)
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.