| 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
-
- Box-Cox transform is perceived as a useful data (pre)processing technique used to stabilize variance and make the data more normally distributed.
- 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.
- The BOXCOX function accepts a single value or an array of values for X.
- The shift parameter must be large enough to make all the values of X positive.
- Requirements
-
Header SFSDK.H Library SFSDK.LIB DLL SFSDK.DLL
- 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
-
- Box-Cox transform is perceived as a useful data (pre)processing technique used to stabilize variance and make the data more normally distributed.
- 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.
- The BOXCOX function accepts a single value or an array of values for X.
- 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 Class SFSDK Scope Public Lifetime Static Package 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
