Here, you can use this cheat sheet for ARIMA.(https://www.datascience.com/blog/introduction-to-forecasting-with-arima-in-r-learn-data-science-tutorials)
- Examine your data
Plot the data and examine its patterns and irregularities.
Clean up any outliers or missing values if needed.
tsclean() is a convenient method for outlier removal and inputing missing values.
Take a logarithm of a series to help stabilize a strong growth trend.
- Decompose your data
Does the series appear to have trends or seasonality?
Use decompose() or stl() to examine and possibly remove components of the series.
Is the series stationary?
Use adf.test(), ACF, PACF plots to determine order of differencing needed.
- Autocorrelations and choosing model order
Choose order of the ARIMA by examining ACF and PACF plots
- Fit an ARIMA model
- Evaluate and iterate
Check residuals, which should haven no patterns and be normally distributed
If there are visible patterns or bias, plot ACF/PACF. Are any additional order parameters needed?
Refit model if needed. Compare model errors and fit criteria such as AIC or BIC.
Calculate forecast using the chosen model.
As the others mentioned, you can use "forecast" package after your model is built.