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Will
Will
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In response to Question 1:

The author of the article you mentioned probably used the ts_log_diff transformations to show the forecasting examples because this the most common transformation for most data sets (i.e most generalizable).

EDIT:

It is my understanding that the ARIMA models are used in cases where stationarity cannot be achieved as the author has pointed out in the original post in Section 5: Forecasting a Time Series:

"A series with significant dependence among values. In this case we need to use some statistical models like ARIMA to forecast the data."

In response to Question 1:

The author of the article you mentioned probably used the ts_log_diff transformations to show the forecasting examples because this the most common transformation for most data sets (i.e most generalizable).

In response to Question 1:

The author of the article you mentioned probably used the ts_log_diff transformations to show the forecasting examples because this the most common transformation for most data sets (i.e most generalizable).

EDIT:

It is my understanding that the ARIMA models are used in cases where stationarity cannot be achieved as the author has pointed out in the original post in Section 5: Forecasting a Time Series:

"A series with significant dependence among values. In this case we need to use some statistical models like ARIMA to forecast the data."

Source Link
Will
Will
  • 101
  • 2

In response to Question 1:

The author of the article you mentioned probably used the ts_log_diff transformations to show the forecasting examples because this the most common transformation for most data sets (i.e most generalizable).