arXiv subjects

Chika Yinka-Banjo

Publications and source records attributed to Chika Yinka-Banjo.

3 recordsLinked to original sources

arXiv2026

Time Series Based CO2 Emission Forecasting and Energy Mix Analysis for Net Zero Transitions: A Multi Country Study

This study examines long-term CO$_2$ emission trajectories across five major economies: Nigeria, the United States, China, Brazil, and Russia, by integrating national energy-mix characteristics with time-series forecasting models. Annual emissions from 2000-2023 were analyzed alongside energy production data to classify countries into fossil-dependent, transition-phase, or renewable-accelerated profiles. Three forecasting models (ARIMA, SARIMA, and Holt-Winters exponential smoothing) were evaluated using MAE, RMSE, MAPE, and R$^2$ metrics. Results show that Holt-Winters provided the most accurate forecasts for Nigeria, the United States, China, and Brazil, while SARIMA performed best for Russia due to its relatively stable emissions. Long-term projections from 2024 to 2060 indicate divergent decarbonization pathways. Brazil aligns most closely with a low-emission future owing to its renewable-dominant energy system, whereas Nigeria continues on an upward emissions trajectory driven by fossil dependence. The United States and China maintain moderate declines but require accelerated mitigation to reach their respective net-zero commitments. Russia's emissions remain largely flat under current conditions. These findings highlight the strong influence of energy structures on national decarbonization prospects and underscore the need for targeted energy policy reforms to align with global climate objectives.

eess.SY↗

arXiv2024

Comparative analysis of 3D-CNN models, GARCH-ANN, and VAR models for determining equity prices

Financial models have increasingly become popular in recent times, and the focus of researchers has been to find the perfect model which fits all circumstances; however, this has not been thoroughly achieved, and as a result, many financial models have been created. Artificial Intelligence modelling has increasingly become more popular in the financial space as an answer to the weakness of the advanced mathematical models studied in Economics. This paper introduces three commonly used models and tests them on the S&P500 to give a strong projection as to the future values of the prices. It then introduces various error metrics like the root mean square error (RMSE) to ascertain the viability of the models. The results show that a longer-term forecast indeed has more arduous consequences as there is a veer between the actual and the forecasted readings. The models can produce a strong growth estimate for businesses and policymakers to plan more appropriately in the short term. Overall, the GARCH-LSTM model produced the least RMSE which also shows that complex models tend to perform better than simpler models

cs.CE↗

arXiv2020

Estimating the time-lapse between medical insurance reimbursement with non-parametric regression models

Non-parametric supervised learning algorithms represent a succinct class of supervised learning algorithms where the learning parameters are highly flexible and whose values are directly dependent on the size of the training data. In this paper, we comparatively study the properties of four nonparametric algorithms, K-Nearest Neighbours (KNNs), Support Vector Machines (SVMs), Decision trees and Random forests. The supervised learning task is a regression estimate of the time-lapse in medical insurance reimbursement. Our study is concerned precisely with how well each of the nonparametric regression models fits the training data. We quantify the goodness of fit using the R-squared metric. The results are presented with a focus on the effect of the size of the training data, the feature space dimension and hyperparameter optimization.

cs.LG↗