# NDK_BOXCOX int __stdcall NDK_BOXCOX ( double * X, size_t N, double * lambda, double * alpha, int retTYpe, double * retVal )

Computes the Box-Cox transformation, including its inverse.

Returns
status code of the operation
Return values
 NDK_SUCCESS Operation successful NDK_FAILED Operation unsuccessful. See Macros for full list.
Parameters
[in,out] X is the univariate time series data (a one dimensional array).
[in] N is the number of observations in X.
[in] lambda is the input power parameter of the transformation, on a scale from 1 to 0. If omitted, a default value of 0 is assumed.
[in] alpha is the input shift parameter for X. If omitted, the default value is 0.
[in] retTYpe is a number that determines the type of return value: 1 (or missing)=Box-Cox, 2=inverse Box-Cox, 3= LLF of Box-Cox.
Value Return Type
1 or omitted   Box-Cox Transform
2 Inverse of Box-Cox transform
3 LLF of Box-Cox transform
[out] retVal is the calculated log-likelihood value of the transform (retType=3).
Remarks
1. Box-Cox transform is perceived as a useful data (pre)processing technique used to stabilize variance and make the data more normally distributed.
2. The Box-Cox transformation is defined as follows: $T\left ( x_{t}; \lambda, \alpha \right ) = \begin{cases} \dfrac{\left ( x_{t} + \alpha \right )^{\lambda}-1}{\lambda} & \text{ if } \lambda \neq 0 \\ \log \left ( x_t + \alpha \right ) & \text{ if } \lambda= 0 \end{cases}$ Where:
• $$x_{t}$$ is the value of the input time series at time $$t$$
• $$\lambda$$ is the input scalar value of the Box-Cox transformation
• $$\alpha$$ is the shift parameter
• $$\left(x_t +\alpha \right) \gt 0$$ for all t values.
3. The BOXCOX function accepts a single value or an array of values for X.
4. The shift parameter must be large enough to make all the values of X positive.
Requirements
Examples



 Namespace: NumXLAPI Class: SFSDK Scope: Public Lifetime: Static
 int NDK_BOXCOX ( double[] pData, UIntPtr nSize, out double lambda, out double fAlpha, int argRetType, out double retVal )

Computes the Box-Cox transformation, including its inverse.

Returns
status code of the operation
Return values
 NDK_SUCCESS Operation successful NDK_FAILED Operation unsuccessful. See Macros for full list.
Parameters
[in,out] pData is the univariate time series data (a one dimensional array).
[in] nSize is the number of observations in pData.
[in] lambda is the input power parameter of the transformation, on a scale from 1 to 0. If omitted, a default value of 0 is assumed.
[in] fAlpha is the input shift parameter for pData. If omitted, the default value is 0.
[in] argRetType is a number that determines the type of return value: 1 (or missing)=Box-Cox, 2=inverse Box-Cox, 3= LLF of Box-Cox.
Value Return Type
1 or omitted   Box-Cox Transform
2 Inverse of Box-Cox transform
3 LLF of Box-Cox transform
[out] retVal is the calculated log-likelihood value of the transform (retType=3).
Remarks
1. Box-Cox transform is perceived as a useful data (pre)processing technique used to stabilize variance and make the data more normally distributed.
2. The Box-Cox transformation is defined as follows: $T\left ( x_{t}; \lambda, \alpha \right ) = \begin{cases} \dfrac{\left ( x_{t} + \alpha \right )^{\lambda}-1}{\lambda} & \text{ if } \lambda \neq 0 \\ \log \left ( x_t + \alpha \right ) & \text{ if } \lambda= 0 \end{cases}$ Where:
• $$x_{t}$$ is the value of the input time series at time $$t$$
• $$\lambda$$ is the input scalar value of the Box-Cox transformation
• $$\alpha$$ is the shift parameter
• $$\left(x_t +\alpha \right) \gt 0$$ for all t values.
3. The BOXCOX function accepts a single value or an array of values for X.
4. The shift parameter must be large enough to make all the values of X positive.
Exceptions
Exception Type Condition
None N/A
Requirements
Namespace NumXLAPI SFSDK Public Static NumXLAPI.DLL
Examples

References
* Hamilton, J .D.; Time Series Analysis , Princeton University Press (1994), ISBN 0-691-04289-6
* Tsay, Ruey S.; Analysis of Financial Time Series John Wiley & SONS. (2005), ISBN 0-471-690740
* D. S.G. Pollock; Handbook of Time Series Analysis, Signal Processing, and Dynamics; Academic Press; Har/Cdr edition(Nov 17, 1999), ISBN: 125609906
* Box, Jenkins and Reisel; Time Series Analysis: Forecasting and Control; John Wiley & SONS.; 4th edition(Jun 30, 2008), ISBN: 470272848