| Financial Time Series | |
Syntax
[transdat, lambda] = boxcox(data) [transdat, lambda] = boxcox(tsobj) transdat = boxcox(data) transdat = boxcox(tsobj)
Arguments
|
Data vector. Must be positive. |
tsobj |
Financial time series object |
Description
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
The logarithm is the natural logarithm (log base e). The algorithm calls for finding the 
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 calculates the transformation parameter .
[transdat, lambda] = boxcox(tsojb)
transforms the financial time series object tsobj using the Box-Cox transformation method into transdat.
If the input data is a vector, transdat is also a vector. If the input is a financial time series object, transdat is likewise a financial time series object.
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, e.g., if the object contains series names Open and Close, lambda has fields lambda.Open and lambda.Close.
transdat = boxcox(lambda, data) and
transdat = boxcox(lambda, tsobj) transform the data using a certain specified for the Box-Cox transformation. This syntax does not find the optimum that maximizes the LLF.
See Also
| | bollinger | busdays | |