I need help analyzing the beer production data using ARIMA models $($the data set is in fpp$2$ and fpp$3).$ Please follow the steps below to complete the analysis and provide detailed explanations along the way.

Data Preparation:

Load the beer production data from the provided dataset $($beer$\_$production.csv$).$

Ensure the data is in a time series format suitable for analysis.

Plot Historical Data:

Generate a time series plot for the beer production data to visualize trends and seasonality.

Provide the R code used to generate the plot.

ACF and PACF Plots:

Generate ACF $($Autocorrelation Function$)$ and PACF $($Partial Autocorrelation Function$)$ plots for the beer production data.

Use these plots to identify appropriate lags for the ARIMA model.

Provide the R code used to generate these plots.

Fit ARIMA Model:

Based on the ACF and PACF plots, fit an ARIMA model to the beer production data. Consider seasonal components as needed.

Provide the summary of the fitted ARIMA model, including estimated coefficients and diagnostic checks.

Provide the R code used to fit the ARIMA model.

Generate Forecast:

Using the fitted ARIMA model, generate forecasts for beer production for the next $10$ quarters.

Plot the forecasts along with the historical data, including prediction intervals.

Provide the R code used to generate the forecast and plot.

Discussion:

Discuss the results of the ARIMA model, including how well it captures the trend and seasonality in the beer production data.

Explain the forecast results and the prediction intervals.

Requirements:

Use R and the fpp$3$ package for all analyses.

Provide clear and concise explanations for each step.

Include all relevant plots and model summaries in the answer.

Ensure the R code is well$-$commented and easy to follow.

Additional Information:

Data snippet for beer production $($if needed for reference$)$:

Quarter Beer

$1956$ Q$1284$

$1956$ Q$2213$

$1956$ Q$3227$

$1956$ Q$4308$

$......$

$2010$ Q$2374$

I need the expert to provide a comprehensive answer that ensures clarity and accuracy, with all relevant plots and model summaries included. Thank you!