A blending ensemble learning model for crude oil price forecasting
Mahmudul Hasan,
Mohammad Zoynul Abedin,
Petr Hajek
et al.
Abstract:To efficiently capture diverse fluctuation profiles in forecasting crude oil prices, we here propose to combine heterogenous predictors for forecasting the prices of crude oil. Specifically, a forecasting model is developed using blended ensemble learning that combines various machine learning methods, including k-nearest neighbor regression, regression trees, linear regression, ridge regression, and support vector regression. Data for Brent and WTI crude oil prices at various time series frequencies are used … Show more
In the realm of well cost estimation, the accurate forecasting of spread rates is pivotal, given the substantial financial implications of erroneous assumptions. This paper, "Spread Rate Forecasting in Well Cost Estimation – A Study of Methods and Applications," delves into the uncertainty inherent. Through a thorough examination of predictive methodologies, the research harnesses both econometric and machine learning models, which are commonly utilized in forecasting crude oil prices. The study formulates models based on publicly available data, such as ‘West Texas Intermediate’ (WTI) and the ‘Baker Hughes Rig Count’, to predict the Spread Rate. The empirical results underscore the efficacy of the proposed models, with the predicted spread rates closely mirroring actual figures. Notably, the models’ precision wanes when extending the forecast horizon beyond a year, a limitation accentuated by the unforeseen WTI and Spread Cost fluctuations during the COVID-19 pandemic. A comparative analysis shows the superiority of RNN, LSTM, Bayesian, and OLS models over the ARIMA model, evidenced by lower Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) metrics. The paper advocates for a probabilistic approach to navigate the uncertainties prevalent in long-term forecasting endeavors.
Introduction. High concentration of PM2.5 has the adverse effect on people's health. According to the evaluations made by the researchers, the impact of the particulate matter from the construction dust emissions resulted in 18% of deaths from respiratory diseases. Due to the growth of construction production volume and consequent increase of dust emission volumes, there arises the need to expand the scope of using the end-to-end technologies, namely the artificial intelligence technologies, for predicting the fine-dispersed dust particles PM2.5 concentration in dust emissions at the construction site.Materials and methods. To achieve this goal, the measurements of PM2.5 concentration at the construction site were carried out using the Handheld 3016 IAQ particle counter in the period from July 1 to July 6, 2023 taking into account the meteorological characteristics of the territory, which then became the input data for modelling the forecast of dust pollution concentration using such algorithms as ARIMA, EMA, XGBoost, etc., and the ensemble models that included the above machine learning algorithms. The efficiency of using these technologies for predicting was determined by comparing the results of the forecast and the field measurements data.Results. A correlation analysis was performed using the Modeltime program, which determined the relationship between PM2.5 concentration and meteorological variables. Autocorrelation was performed using Pearson correlation. At the first stage, four one-dimensional models based on the artificial intelligence were evaluated to determine the accuracy of mean concentration forecast. The next step was to evaluate the capacity of predicting the mean PM2.5 concentration using the multidimensional models that took into account the relationships between the independent and dependent variables. At the final stage of the research, three most efficient predictive models were included to test the ensemble model.Discussion and conclusion. The reliable predictive models can be the useful tools for understanding the concentration impact factors. In the present research, seven machine learning algorithms were used to predict the concentration of PM2.5. The research, as a whole, presents the evidences of the integrated modeling method efficiency for predicting the air pollution.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.