# Ensemble Machine Learning for Monkeypox Transmission Time Series Forecasting
## 4 Conclusions  
This research presents a novel technique that uses stacking ensemble learning for monkey-box time series forecasting. The COVID-19 and monkey-box datasets were used for training and testing the proposed method, respectively. Experiment results show that the proposed stacking ensemble learning outperformed other machine learning techniques considered in the paper. In addition, the three proposed training strategies--OLS, LASSO, and RIDGE--achieved the lowest computational training time. Despite only being tested with two source datasets, the proposed method can also be used to train more datasets. According to experiment findings, compared to other continents, Europe has the highest rate of monkey box, and, unless necessary measures are taken, there is a probability that the pandemic will spread faster. The stacking ensemble learning method was adopted for this paper because it saves us the trouble of having to train several machine learning models from scratch to fulfill similar tasks, therefore saving time and resources. SEL also serves as a cost-cutting measure in areas of machine learning that need a lot of resources,  
Figure 15: Time Series of Stacking Ensemble Learning (SEL). \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Algorithm** & **RMSE** & **MSE** & **MAE** \\ \hline Adaboost & 100.7981 & 10,160.2726 & 68.5727 \\ \hline GBOOST & 115.8856 & 13,429.4795 & 75.7660 \\ \hline Random Forest & 111.1798 & 12,360.9503 & 73.9469 \\ \hline OLS & 108.4988 & 11,772.0099 & 74.2072 \\ \hline LASSO & 124.5257 & 15,506.6696 & 82.3904 \\ \hline RIDGE & 113.4759 & 12,876.7957 & 70.2174 \\ \hline SEL & 33.1075 & 1096.1068 & 22.4214 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance Metrics of Machine Learning and Proposed Stacking Ensemble Learning (SEL). such as image classification or natural language processing. Moreover, it is very useful in compensating for a shortage of labeled training data maintained by an organization, as pre-trained models are used. Stacking ensemble learning makes use of minimal computational resources and helps attain enhanced results using a smaller dataset. Furthermore, stacking ensemble learning models attains optimal performance quickly compared to conventional ML models. The reason for this is that the models leverage knowledge from base models and meta models. Our findings from experimental results indicated that Europe has the highest monkey box transmission rate with 72.2%, followed by South America with 12.7%. The continent of Africa now has the lowest transmission rate at 0.8%. Based on the time series prediction of the monkey pox dataset used for this study, the transmission rate will decrease. This indicates that monkey pox transmission is steadily declining in Europe and other parts of the world. In the future, deep neural network algorithms such as deep belief networks, convolutional neural networks, generative adversarial networks, and autoencoders will be applied to detecting and diagnosing monkey pox infections using image datasets. Conceptualization, E.G.D., D.O.O., S.B.J. and O.O.O. ; methodology, E.G.D., D.O.O., S.B.J., O.O.O. ; software, E.G.D., D.O.O. and S.B.J. ; validation, E.G.D., D.O.O., S.B.J., O.O.O. and O.E. ; formal analysis, E.G.D., D.O.O., S.B.J. and O.O.O. ; investigation, E.G.D., D.O.O., S.B.J. and O.O.O.O. ; resources, E.G.D., D.O.O., S.B.J., O.O.O. and O.E. ; data curation, E.G.D., D.O.O., S.B.J. and O.O.O.O. ; writing--original draft preparation, E.G.D., D.O.O., S.B.J. and O.O.O. ; writing--review and editing, E.G.D., D.O.O., S.B.J., O.O.O. and O.E. ; visualization, E.G.D., D.O.O., S.B.J., O.O.O. and O.E. ; supervision, E.G.D. and O.O.O. ; project administration, E.G.D. All authors have read and agreed to the published version of the manuscript. The APC was funded by Virginia Tech University's research support to O.E. O.O.O. received research funding from the Oppenheimer Memorial Trust (OMT) Foundation with grant number: OMT Ref. 21563/01); O.O.O.