We have been mentioning about some models which are applicable on only time series data, which does
not have trend and seasonality, but there is a special term for such cities such these are called stationary
cities.
Well, technically, a stationary time series is one whose statistical properties, such as mean variance,
autocorrelation, etc., are all constant over time.
For most business and economic activities, that Time series data is a fairly stationary.
For example, if you look at the mean of the monthly sales of a company for 2010 and then for 2019,
most probably the means will be different.
That company would have either grown or declined in sales performance over 10.
So since the mean is changing over time, this time series is not stationary.
And we can not apply models such as we are or at Imar to forecast it.
So to make a time series stationary, we just need to use different thing, as we have shown you earlier.
We will use different thing to remove trend and seasonality book.
And for most purposes, we will be able to use our models on the resulting series.
And you can simply use models such as Satima or Sa'adi Max, which handles seasonality and trend.