# NDK_AIRLINE_PARAM

 int __stdcall NDK_AIRLINE_PARAM ( double * pData, size_t nSize, double * mean, double * sigma, WORD S, double * theta, double * theta2, MODEL_RETVAL_FUNC retType, size_t maxIter )

Returns an array of cells for the initial (non-optimal), optimal or standard errors of the model's parameters.

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,out] mean  is the model mean (i.e. mu).
[in,out] sigma  is the standard deviation of the model's residuals/innovations.
[in] S is the length of seasonality (expressed in terms of lags, where s > 1).
[in,out] theta  is the coefficient of first-lagged innovation (see model description).
[in,out] theta2  is the coefficient of s-lagged innovation (see model description
[in] retType  is a switch to select the type of value returned: 1= Quick Guess, 2=Calibrated, 3= Std. Errors
Order   Description
1 Quick guess (non-optimal) of parameters values (default)
2 Calibrated (optimal) values for the model's parameters
3 Standard error of the parameters' values
[in] maxIter  is the maximum number of iterations used to calibrate the model. If missing or less than 100, the default maximum of 100 is assumed.
Remarks
1. The underlying model is described here.
2. The time series is homogeneous or equally spaced
3. The time series may include missing values (e.g. NaN) at either end.
4. NDK_AIRLINE_PARAM returns an array of the values (or errors) of the model's parameters in the following order:
• $$\mu$$
• $$\theta$$
• $$\Theta$$
• $$\sigma$$
5. The AIRLINE_GUESS sets the $$\mu$$ and $$\sigma$$ equal to the differenced sample (i.e. $$Z_t=(1-L)(1-L^s)Y_t$$) average, and standard deviation respectively, and it sets the $$\theta = 0$$ and $$\Theta=0$$
Requirements
Examples

 Namespace: NumXLAPI Class: SFSDK Scope: Public Lifetime: Static
 int __stdcall NDK_AIRLINE_PARAM ( double[] pData, UIntPtr nSize, ref double mean, ref double sigma, short dSeason, ref double theta, ref double theta2, MODEL_RETVAL_FUNC retType, UIntPtr maxIter )

Returns an array of cells for the initial (non-optimal), optimal or standard errors of the model's parameters.

Return Value

a value from NDK_RETCODE enumeration for the status of the call.

 NDK_SUCCESS operation successful Error Error Code
Parameters
[in,out] pData is the univariate time series data (a one dimensional array).
[in] nSize is the number of observations in pData.
[in,out] mean  is the model mean (i.e. mu).
[in,out] sigma  is the standard deviation of the model's residuals/innovations.
[in] dSeason is the length of seasonality (expressed in terms of lags, where s > 1).
[in,out] theta  is the coefficient of first-lagged innovation (see model description).
[in,out] theta2  is the coefficient of s-lagged innovation (see model description
[in] retType  is a switch to select the type of value returned: 1= Quick Guess, 2=Calibrated, 3= Std. Errors
Order   Description
1 Quick guess (non-optimal) of parameters values (default)
2 Calibrated (optimal) values for the model's parameters
3 Standard error of the parameters' values
[in] maxIter  is the maximum number of iterations used to calibrate the model. If missing or less than 100, the default maximum of 100 is assumed.
Remarks
1. The underlying model is described here.
2. The time series is homogeneous or equally spaced
3. The time series may include missing values (e.g. NaN) at either end.
4. NDK_AIRLINE_PARAM returns an array of the values (or errors) of the model's parameters in the following order:
• $$\mu$$
• $$\theta$$
• $$\Theta$$
• $$\sigma$$
5. The AIRLINE_GUESS sets the $$\mu$$ and $$\sigma$$ equal to the differenced sample (i.e. $$Z_t=(1-L)(1-L^s)Y_t$$) average, and standard deviation respectively, and it sets the $$\theta = 0$$ and $$\Theta=0$$
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