Box-Cox transformation
boxcox
has been partially removed and will no longer accept a
fints
object
(tsobj
).
Replace all instances of a fints
object (tsobj
)
for input with an array by using fts2timetable
to convert a
fints
object to a timetable
object and then
use timetable2table
and table2array
.
[transdat,lambda] = boxcox(data) [transfts,lambda] = boxcox(tsobj) transdat = boxcox(lambda,data) transfts = boxcox(lambda,tsobj)
| Data vector. Must be positive and specified as a column data vector. |
| Financial time series object. |
boxcox
transforms nonnormally distributed
data to a set of data that has approximately normal distribution.
The Box-Cox transformation is a family of power transformations.
If λ is not = 0
, then
If λ is = 0
, then
The logarithm is the natural logarithm (log base e). The algorithm
calls for finding the λ value that maximizes the Log-Likelihood
Function (LLF). The search is conducted using fminsearch
.
[transdat,lambda] = boxcox(data)
transforms
the data vector data
using the Box-Cox transformation
method into transdat
. It also estimates the transformation
parameter λ.
[transfts,lambda] = boxcox(tsojb)
transforms
the financial time series object tsobj
using the
Box-Cox transformation method into transfts
. It
also estimates the transformation parameter λ.
If the input data is a vector, lambda
is
a scalar. If the input is a financial time series object, lambda
is
a structure with fields similar to the components of the object; for
example, if the object contains series names Open
and Close
, lambda
has
fields lambda.Open
and lambda.Close
.
transdat = boxcox(lambda, data)
and transfts
= boxcox(lambda, tsobj)
transform the data using a certain
specified λ for the Box-Cox transformation. This syntax does
not find the optimum λ that maximizes the LLF.