Download the daily adjusted close price data for any one company $($of your choice$)$ from the table given below, for the period from January $1,2023,$ to December $31,2023($One year$).$ You need to mention the rationale for choosing the company as well, in brief. Compute the log returns and estimate the appropriate ARIMA model using the Box$-$Jenkins methodology. Please show these computations and output:

$1\mathrm{.}1.$ Keep the last $5$ observations of the returns series as the out$-$of$-$sample forecast. Fit a suitable ARIMA model on the remaining observations $($in$-$sample$)$ of the log returns $($N$-5).$

$1\mathrm{.}2.$ You are supposed to show the entire procedure $($Box$-$Jenkins$)$ involved in fitting the suitable ARIMA model $($along with diagnostics$),$ forecasting the model, and checking the accuracy of the model.

$1\mathrm{.}3.$ If you observe that the return series of the chosen company exhibits characteristics of a white noise process, undertake the fitting of ARIMA $(1,0,1)$ on the return series. This step aims to elucidate the comprehensive procedure involved in the entire ARIMA modelling process.

$1\mathrm{.}4.$ After identifying the suitable ARIMA model, do forecasting for the last $5$ observations and report the accuracy of the model $($out$-$of$-$sample only$).$

$1\mathrm{.}5.$ Please report all important output$/$results of R $($Such as line chart of price and return series, correlograms, stationarity test results, Ljung Box test results, among others$)$ as screenshots$/$images in the assignment.