[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.