Deep understanding of the ARIMA model

It is worth noting that the observed data is uniquely orderly according to the time of observation, but it doesn't have to be dependent on time, i.e. time (index of the observations) doesn't have to be one of the independent variables. Stationarity: a stationary process is a stochastic process, whose mean, variance and autocorrelation structure do not change over time. It can also be defined formally using mathematical terms, but in this article, it's not necessary. Intuitively, if a time series is stationary, we look at some parts of them, they should be very similar -- the time series is flat looking and the shape doesn't depend on the shift of time. It surely isn't, since it's not stochastic, stationarity is not one of its properties) Figure 1.1 shows the simplest example of a stationary process -- white noise.