首页|Studies from Sidi Mohamed Ben Abdellah University Further Understanding of Machi ne Learning (Modelling Stock Prices of Energy Sector using Supervised Machine Le arning Techniques)
Studies from Sidi Mohamed Ben Abdellah University Further Understanding of Machi ne Learning (Modelling Stock Prices of Energy Sector using Supervised Machine Le arning Techniques)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on artificial in telligence. According to news originating from Fes, Morocco, by NewsRx editors, the research stated, "This paper aims at comparing the performance of the differ ent state-of-the-art machine learning techniques in anticipating the performance of stock prices of the energy sector." The news editors obtained a quote from the research from Sidi Mohamed Ben Abdell ah University: "The data collected cover the period from January 2020 to Februar y 2023 with a daily frequency for the three most imported refined petroleum prod ucts in Morocco and trained four regression machines learning (linear regression , lasso regression, ridge regression, and SVR) and four classifiers machine lear ning (logistic regression, decision tree, extra tree and Random Forest) so that anticipating one day ahead prices direction can take place no matter whether the y are negative or positive prices. The performance of regression algorithm is th en evaluated using different evaluation metrics, especially MSE, RMSE, MAE, MAPE and R2 to evaluate the performance of regression algorithm while precision, rec all and F1 scores are used to evaluate the performance of classifiers algorithm. The outcomes propose that the performance of linear regression and ridge regres sion takes place equally and outperform other single regression that is lasso re gression and SVR for-one-day predictions as a whole. In addition to that, we hav e come to find that in the classifiers, algorithms group all machine learning di splay similar predictive accuracy, this is on one hand. On the other hand, the b est of them is the logistic regression. In brief, this study suggests that all p erformance metrics are significantly improved by ensemble learning."
Sidi Mohamed Ben Abdellah UniversityFe sMoroccoAlgorithmsCyborgsEmerging TechnologiesFinance and InvestmentInvestment and FinanceMachine Learning
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