an overview of the applications of box-cox transformation in a time series

an overview of the applications of box-cox transformation in a time series

in all areas of statistics, one of the most important issues is data collection. sometimes the data collected does not follow the normal distribution and tests were performed to see if the data followed the normal distribution, the most important of these are the shapiro-wilk test and the kolmogorov-smirnov test. if the data is abnormal, one of the most suitable methods used for data normalization is the box-cox method, which is widely used in the time series. in addition to normalizing the data, this method can help maintain the stability of the variance in the time series.

in this article, we will first introduce the box-cox method and then introduce similar methods for normalizing data, which are four methods. finally, we will examine the application of these transformations in a time series in r software.