Subhasree Chatterjee shows us how to use R to implement an ARIMA model:

Once the data is ready and satisfies all the assumptions of modeling, to determine the order of the model to be fitted to the data, we need three variables: p, d, and q which are non-negative integers that refer to the order of the autoregressive, integrated, and moving average parts of the model respectively.

To examine which p and q values will be appropriate we need to run

`acf()`

and`pacf()`

function.

`pacf()`

at lag k is autocorrelation function which describes the correlation between all data points that are exactly k steps apart- after accounting for their correlation with the data between those k steps. It helps to identify the number of autoregression (AR) coefficients(p-value) in an ARIMA model.

ARIMA feels like it should be too simple to work, but it does.

Kevin Feasel

2018-02-02

Data Science, R