Applying power transformations

Time series data can be complex, and embedded within the data is critical information that you will need to understand and peek into to determine the best approach for building a model. For example, you have explored time series decomposition, understood the impact of trend and seasonality, and tested for stationarity. In the previous recipe, Detecting time series stationarity, you examined the technique to transform data from non-stationary to stationary. This includes the idea of detrending, which attempts to stabilize the mean over time.

Depending on the model and analysis you are pursuing, you may need to test for additional assumptions against the observed dataset or the model's residuals. For example, testing for homoskedasticity (also spelled homoscedasticity) and normality. Homoskedasticity means that the variance is stable over time. More specifically, it is the variance of the residuals. When the variance is not constant, changing over time...