# 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] S 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
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 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