Please solve using R Studio.

$1\mathrm{.}1.$ Create the data set $$us$\_$wholesale$\_$emp$$ for the time series $$Wholesale Trade$$ from the data set $$us$\_$employment$$ that covers the period from January $1990$ to the end of the time series. Create a plot of the time series. The plot must include a title, and the axis labels must show the appropriate units. Attach the plot below.

$1\mathrm{.}2.$ Compute the autocorrelation coefficients for US wholesale trade employment up to lag $36.$ Attach the autocorrelation tsibble below. Use font $$courier new$$ and size $8$ to format the table.

$1\mathrm{.}3.$ Create a plot of the ACF for US wholesale trade employment. Explain the ACF.

$2\mathrm{.}1$ Do an STL decomposition for US wholesale trade series you created in problem $1.$ Use a trend$($window $=7)$ and season $($window $=$periodic$).$ Attach the plot below.

$2\mathrm{.}2$ Print the dable that shows the STL components and attach it below. Use font $$courier new$$ and size $8$ to format the table.

$3\mathrm{.}1$ Create a train data set for the period from $1990$ Jan to $2017$ Dec from the US wholesale employment series that you created in problem $1.$ Fit the models mean, na$$ve$,$ seasonal na$$ve and drift to the train data set. Generate $21$ steps ahead forecasts and plot the forecasts against the actual values of the US wholesale employment series. Attach the forecast plot below. Identify the best method based on the figure.

$3\mathrm{.}2$ Do an STL decomposition of the US wholesale employment series and use the na$$ve method to forecast the seasonally adjusted component. Generate a forecast plot and attach it below.

$3\mathrm{.}3$ Re$-$seasonalize the seasonally adjusted forecasts of section $3\mathrm{.}2,$ generate new forecasts and plot them. Attach a table with the forecasts and the plot below.

$4\mathrm{.}1$ Fit the models Mean, Na$$ve$,$ Seasonal Na$$ve$,$ and Drift to the train data set you created in problem $3.$ Generate forecasts for the period January $2018-$ September $2019.$ Generate a plot of the forecasts and the actual data. Attach the plot below.

$4\mathrm{.}2$ Generate accuracy measures and identify the best forecasting model. Paste the accuracy measures here. Use font courier new and size $8.$

$4\mathrm{.}3$ Generate a plot of the residual diagnostics for the best model in $4\mathrm{.}2.$ Explain the residual diagnostics. Attach the plot here.

$4\mathrm{.}4$ Do the Box$-$Pierce and Ljung$-$Box correlation tests of the residuals. Explain the results. Attach the results here. Use font courier new and size $8.$