Forecast the sales of one online store with ARIMA. Download the dataset sales.csv and do the following work:
ARIMA R Script also attached

1. Load the data into R, and draw the sales in time series with raw data.
2. Properly clean the data, and draw the sales in time series with cleaned data.
3. Draw the Weekly Moving Average and Monthly Moving Average of the sales.
4. Decompose and draw the sales into the seasonal, trend, and cycle sections.
5. Conduct the Augmented Dickey-Fuller Test for the sales. Is the time series data stationary?
6. Build the ARIMA (1,0,1) model, and show the regressions outputs.
7. Build the ARIMA (1,0,7) model, and show the regressions outputs.
8. Use the ARIMA (1,0,7) model to predict the sales within 30 days (with seasonality). Show the diagram for the forecasting.

NOTE: you might replace the scripts on Line 40 in R tutorial with the follow ones:
daily_data$Date = as.Date(daily_data$dteday, "%m/%d/%y")  #To make sure the data sa Date Type in R.