Bidyut Ghosh explains how the ARIMA forecasting method works:

The earlier models of time series are based on the assumptions that the time series variable is stationary (at least in the weak sense).

But in practical, most of the time series variables will be non-stationary in nature and they are intergrated series.

This implies that you need to take either the first or second difference of the non-stationary time series to convert them into stationary.

Bidyut ends with a little bit of implementation in R, but I’d guess that’ll be the focus of part 2.

Kevin Feasel

2018-04-17

Data Science, R