The objective of this paper is to construct univariate and bivariate blending type -Schurer–Kantorovich operators depending on two parameters and to approximate a class of measurable functions on . We present some auxiliary results and obtain the rate of convergence of these operators. Next, we study the global and local approximation properties in terms of first- and second-order modulus of smoothness, weight functions, and by Peetre’s -functional in different function spaces. Moreover, we present some study on numerical and graphical analysis for our operators.

Crude oil is a widely recognized, indispensable global and national economic resource. It is significantly susceptible to the boundless fluctuations attributed to various variables. Despite its capacity to sustain the global economic framework, the embedded uncertainties correlated with the crude oil markets present formidable challenges that investors must diligently navigate. In this research, we propose a hybrid machine learning model based on random forest (RF), gated recurrent unit (GRU), conventional neural network (CNN), extreme gradient boosting (XGBoost), functional partial least squares (FPLS), and stacking. This hybrid model facilitates the decision-making process related to the import and export of crude oil in India. The precision and reliability of the different machine learning models utilized in this study were validated through rigorous evaluation using various error metrics, ensuring a thorough assessment of their forecasting capabilities. The conclusive results revealed that the proposed hybrid ensemble model consistently delivered effective and robust predictions compared to the individual models.