## 4 Methodology
### Forecasting Model for COVID-19 Vaccination in the USA
#### 4.4.3 Time Series Forecasting Classifier  
For the time series forecasting model, we needed to set the following parameters.  
* **Forecast Time Unit:** We used two different time units, for the prediction of _Fully Vaccinated Population_. The shorter time period is 1 week (7 days) and the longer time period is 2 months (60 days). By definition, parameter values of 7 days indicate that we are setting up our system to forecast 7 days ahead from the last date of the input training dataset. For the prediction of _partially vaccinated population_, along with these two units, we also predict a middle timeline, that is 39 days, to get the prediction for 4 July, the Independence Day of the USA.
* **Time stamp:** We have a \"Date\" attribute in our dataset. Date was set for the \"Time Stamp\" parameter of our system.
* **Periodicity**: Periodicity allows the user to specify the periodicity of the data. As we predicted the vaccinated population on a daily basis, we chose \"Daily\" for this parameter value.
* **Skip list:** For daily forecasting, it might happen that the entity remains closed on certain days or holidays, for example, businesses, the stock market, etc. For such entities, these time periods do not count. In such cases, \"Skip list\" is used to supply these types of time periods that are not to be considered. For our dataset, we do not  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**No.** & **Feature** & **Description** \\ \hline a & total\_vaccinations & Total number of COVID-19 vaccination doses administered \\ \hline b & people\_vaccinated & Total number of people who received at least one vaccine dose \\ \hline c & people\_fully\_vaccinated & Total number of people who received all doses prescribed by the vaccination protocol \\ \hline d & new\_vaccinations & New COVID-19 vaccination doses administered (only calculated for consecutive days) \\ \hline e & new\_vaccinations\_smoothed & New COVID-19 vaccination doses administered (7-day smoothed) [For countries that do not report vaccination data on a daily basis, it is assumed that vaccination changed equally on a daily basis over any periods in which no data were reported. This produces a complete series of daily figures, which is then averaged over a rolling 7-day window.] \\ \hline f & total\_vaccinations\_per\_hundred & Total number of COVID-19 vaccination doses administered per 100 people in the total population \\ \hline g & people\_vaccinated\_per\_hundred & Total number of people who received at least one vaccine dose per 100 people in the total population \\ \hline h & people\_fully\_vaccinated\_per\_hundred & Total number of people who received all doses prescribed by the vaccination protocol per 100 people in the total population \\ \hline i & new\_vaccinations\_smoothed\_per\_million & New COVID-19 vaccination doses administered (7-day smoothed) per 1,000,000 people in the total population \\ \hline \hline \end{tabular}
\end{table}
Table 4: Features related to COVID-19 vaccination.  
have any such time period for which the regular operation does not continue. Thus, we do not need to set the \"Skip List\" parameter.
* **Confidence intervals:** This parameter is used for the system to compute confidence bounds on the predictions that it makes. We used 95% as the parameter value, which is also the default value of the parameter. The 95% confidence level means that 95% of the true target values fell within the interval. The confidence intervals were computed for each step-ahead level independently.