after differencing variables, how do i do arima in r

After differencing your variables, you can use the arima() function in R to fit an ARIMA model to your differenced time series data. The arima() function requires three inputs: the differenced time series data, the order of the AR, I, and MA terms (p, d, q), and any additional model specifications.

Here is an example of how to use the arima() function after differencing your variables:

# Assuming you have already differenced your variables and stored them in a vector called "differenced_data"

# Fit an ARIMA(1, 1, 1) model to the differenced data
arima_model <- arima(differenced_data, order = c(1, 1, 1))

# Print the model summary
summary(arima_model)

# Make predictions using the ARIMA model
# Assuming you have new data stored in a vector called "new_data"
prediction <- predict(arima_model, n.ahead = length(new_data))

# Print the predicted values
print(prediction)
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In the code above, differenced_data should be replaced with your actual differenced time series data. You can adjust the order of the AR, I, and MA terms by changing the values in the order argument of the arima() function. Additionally, you can specify other model specifications, such as seasonal ARIMA models, by including additional arguments in the arima() function.

Remember to consult the documentation of the arima() function for more details on its usage and available options.