NDK_AIRLINE_VALIDATE

int __stdcall NDK_AIRLINE_VALIDATE ( double  mean,
double  sigma,
WORD  S,
double  theta,
double  theta2 
)

Examines the model's parameters for stability constraints (e.g. stationary, etc.).

Returns
status code of the operation
Return values
NDK_SUCCESS  Operation successful
NDK_FAILED  Operation unsuccessful. See Macros for full list.
Parameters
[in] mean  is the model mean (i.e. mu).
[in] sigma  is the standard deviation of the model's residuals/innovations.
[in] is the length of seasonality (expressed in terms of lags, where s > 1).
[in] theta  is the coefficient of first-lagged innovation (see model description).
[in] theta2  is the coefficient of s-lagged innovation (see model description).
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. The standard deviation (i.e. \(\sigma\)) of the ARMA model's residuals should be greater than zero.
  5. The Airline model is a special case of multiplicative seasonal ARMA model.
  6. The Airline model is a special case of multiplicative seasonal ARIMA model, and it assumes independent and normally distributed residuals with constant variance.
Requirements
Header SFSDK.H
Library SFSDK.LIB
DLL SFSDK.DLL
Examples


   
Namespace:  NumXLAPI
Class:  SFSDK
Scope:  Public
Lifetime:  Static
int NDK_AIRLINE_VALIDATE ( double  mean,
double  sigma,
short  dSeason,
double  theta,
double  theta2 
)

Examines the model's parameters for stability constraints (e.g. stationary, etc.).

Return Value

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

NDK_SUCCESS  operation successful
Error  Error Code
Parameters
[in] mean  is the model mean (i.e. mu).
[in] 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] theta  is the coefficient of first-lagged innovation (see model description).
[in] theta2  is the coefficient of s-lagged innovation (see model description).
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. The standard deviation (i.e. \(\sigma\)) of the ARMA model's residuals should be greater than zero.
  5. The Airline model is a special case of multiplicative seasonal ARMA model.
  6. The Airline model is a special case of multiplicative seasonal ARIMA model, and it assumes independent and normally distributed residuals with constant variance.
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