The TIMEDATA Procedure (Experimental)

Time Series Transformation

Four transformations are available for strictly positive series only. Let ${y_{t} > 0}$ be the original time series, and let ${w_{t}}$ be the transformed series. The transformations are defined as follows:

Log

is the logarithmic transformation.

$w_{t} = \mr {ln}(y_{t})$

$w_{t} = \mr {ln}(y_{t})$

Logistic

is the logistic transformation.

$w_{t} = \mr {ln}(c y_{t} / (1-c y_{t}))$

$w_{t} = \mr {ln}(c y_{t} / (1-c y_{t}))$

where the scaling factor is

$c = (1-10^{-6}) 10 ^{- \mr {ceil}( \mr {log}_{10}({max}( y_{t}) ))}$

$c = (1-10^{-6}) 10 ^{- \mr {ceil}( \mr {log}_{10}({max}( y_{t}) ))}$

and ${\mr {ceil}(x)}$ is the smallest integer greater than or equal to x.

Square root

is the square root transformation.

$w_{t} = \sqrt {y_{t}}$

$w_{t} = \sqrt {y_{t}}$

Box Cox

is the Box-Cox transformation.

$w_{t} = \begin{cases} \frac{y_{t}^{{\lambda }} - 1}{\lambda }, & {\lambda } {\ne } 0 \\ \mr {ln}(y_{t}), & {\lambda } = 0 \end{cases}$

$w_{t} = \begin{cases} \frac{y_{t}^{{\lambda }} - 1}{\lambda }, & {\lambda } {\ne } 0 \\ \mr {ln}(y_{t}), & {\lambda } = 0 \end{cases}$

More complex time series transformations can be performed by using the SAS/ETS EXPAND procedure.