Crude oil in its usage and process brings about several issues that affect the environment including soiling of the air and water and the release of greenhouse gases leading to changes in the climate. Further, historically use of oil has created political and economic stakes that primarily end up in war, nationalism to control resources, and political instability. As a new approach to this problem in this study, we present a novel hybrid of Local Mean Decomposition (LMD) for breaking down the data and contribution coefficients for rebuilding the component with less computational complexity. Using the XGBOOST approach, deep dynamic learning features, augmented with autoregressive moving average (ARIMA), it is possible to predict each reconstructed segment. Turbulences that occur in the oil markets are random, the market shares are sensitive to pressures from other markets, the movement in the market is disorderly, and there is high volatility in the market. Thus, the price of oil is very unfixed, unpredictable, sensitive, and uncertain. Several techniques have been adopted in cost estimation in the aspect of time variation; some of the traditional methods include EMD, EEMD couldn’t solve the computational complexity and save time and cost. Because of the unpredictability and fluctuation observed in oil prices non deep neural network models and data mining cannot be used in the prediction of costs and generalized forecasting. The analysis of the onset of deep non-linear modeling as a part of the machine learning process demonstrates how the discussed hybrid model successfully acquires relevant data and adapts a non-stationary function. The data is modeled using an LMD-SD-ARIMA-XGBOOST hybrid recurrent network when the nonlinearity is considered, and the residual values are forwarded to the XGBOOST model. Besides the volatility problem, the solution provided by the work also addresses the overfitting problem of neural networks. The hybrid LMD-SD-ARIMA-XGBOOST model overcomes overfitting by combining statistical and machine learning components, where the decomposition process isolates key features of the data, reducing noise and complexity. Unlike standalone neural networks, which require large amounts of data to generalize effectively, our hybrid approach leverages ARIMA and XGBOOST models that are inherently less prone to overfitting due to their simpler structures and feature-focused learning. The information that is available to the public according to RMSE, MSE, and MAPE is as follows Information that is available to the public: To validate the proposed hybrid approach, data from the WTI which is publicly available is employed Data from the WTI which is a more recognized benchmark is also employed.

Outliers are observations that are significantly different from the other observations in a dataset. These types of observations are asymmetric in nature due to a lack of symmetry. The estimation of the cumulative distribution function (CDF) is an important statistical measure commonly discussed for symmetric datasets. However, the estimation of the CDF in the case of the asymmetric nature of the dataset is not a much-explored topic. In this article, we use calibration methodology with auxiliary information for modifying the traditional stratification weight, and hence, we obtain efficient estimates of the CDF using robust measures, i.e., mid-range and tri-mean, under the different distance functions. A simulation study is carried out to see the performance of proposed and existing estimators using asymmetric real-life datasets.

Using auxiliary information, the calibration approach modifies the original design weights to enhance the mean estimates. This paper initially proposes two families of estimators based on an adaptation of the estimators presented by recent researchers, and then, it presents a new family of calibration estimators with the set of some calibration constraints under stratified median ranked set sampling (MRSS). The result has also been implemented to the situation of two-stage stratified median ranked set sampling (MRSS). To best of our knowledge, we are presenting for the first time calibration-based mean estimators under stratified MRSS, so the performance evaluation is made between adapted and proposed estimators on behalf of the simulation study with real and artificial datasets. For real-world data or applications, we use information on the body mass index (BMI) of 800 people in Turkey in 2014 as a research variable and age as an auxiliary variable.

Estimating dispersion in populations that are extremely rare, hidden, geographically clustered, and hard to access is a well-known challenge. Conventional sampling approaches tend to overestimate the variance, even though it should be genuinely reduced. In this environment, adaptive cluster sampling is considered to be the most efficient sampling technique as it provides generally a lower variance than the other conventional probability sampling designs for the assessment of rare and geographically gathered population parameters like mean, total, variance, etc. The use of auxiliary data is very common to obtain the precise estimates of the estimators by taking advantage of the correlation between the survey variable and the auxiliary data. In this article, we introduced a generalized estimator for estimating the variance of populations that are rare, hidden, geographically clustered and hard-to-reached. The proposed estimator leverages both actual and transformed auxiliary data through adaptive cluster sampling. The expressions of approximate bias and mean square error of the proposed estimator are derived up to the first-order approximation using Taylor expansion. Some special cases are also obtained using the known parameters associated with the auxiliary variable. The proposed class of estimators is compared with available estimators using simulation and real data applications.