The production and exportation of petroleum plays a dominant role in Nigeria's economy. A drastic fall the quantity crude oil produced is fatal to Nigerian economy since Nigeria depends heavily on sector. Different classical statistical methods have been used model but application machine learning models has grossly understudied. In this study, we identified high-performance between (ARIMA) two (ANN RF) modeling Nigeria. monthly data collected from January, 2006 October, 2020 study secondary...

The price of Brent crude oil is very important to the global economy as it has a huge influence and serves one benchmarks in how other countries organizations value their oil. Few original studies on modeling used predominantly different classical models but application machine learning methods been grossly understudied. In this study, we identified optimal MLMD (MLMD) amongst Support Vector Regression (SVR), Random Forest (RF), Artificial Neural Network (ANN), Deep (DNN) also showed that...

This paper proposes new methods of estimating missing values in time series data while comparing them with existing methods. The are based on the row, column and overall averages arranged a Buys-Ballot table m rows s columns. assume that 1) only one value is at time, 2) trending curve may be linear, quadratic or exponential 3) decomposition method either Additive Multiplicative. performances assessed by accuracy measures (MAE, MAPE RMSE) computed from deviations estimates actual used...

This study modelled the reported daily cumulative confirmed, discharged and death Coronavirus disease 2019 (COVID-19) cases using six econometric models in simple, quadratic, cubic quartic forms an autoregressive integrated moving average (ARIMA) model. The were compared employing R-squared Root Mean Square Error (RMSE). best model was used to forecast COVID-19 for October 2020 February 2021. predicted number of confirmed are alarming. Good planning innovative approaches required prevent...

This study modeled the US Dollar and Nigerian Naira exchange rates during COVID-19 pandemic period using a classical statistical method – Autoregressive Integrated Moving Average (ARIMA) two machine learning methods Artificial Neural Network (ANN) Random Forest (RF). The data were divided into sets namely: training set test set. was used to obtain parameters of model, performance estimated model validated on that served as new data. Though ARIMA random forest performed slightly better than...

Multicollinearity has remained a major problem in regression analysis and should be sustainably addressed. Problems associated with multicollinearity are worse when it occurs at high level among regressors. This review revealed that studies on the subject have focused developing estimators regardless of effect differences levels Studies considered single-estimator combined-estimator approaches without sustainable solution to problems. The possible influence partitioning regressors according...

The British Pound Sterling (GBP) to Nigerian Naira (NGN) exchange rate has been grossly affected by the Coronavirus 2019 (Covid-19) pandemic. It become pertinent identify robust models that will help cope with variability associated Many original studies found ARIMA method be highly useful in modeling and forecasting rates. However, not much work done on GBP NGN during covid-19 pandemic using machine learning models. This study focuses between GPB period of Covid-19 adopting process model...

There is dearth of information in the field statistics on innovative estimation methods that can replace missing values descriptive time series data. Therefore, this review work provides existing and new estimating The insight comparative performance recently-developed ones discussed model structure trending curves as important parameters values. It expected present contribution will assist statisticians seeking to solve problem application should be restricted data with trend (linear,...