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
Header SFSDK.H
Library SFSDK.LIB
DLL SFSDK.DLL
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
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